Voice Recognition
•Download as PPT, PDF•
4 likes•4,298 views
Speech recognition, also known as voice recognition, allows a user to control systems and enter text through speaking. It works by converting analog audio of spoken words to digital signals, then using acoustic and language models to analyze phonemes and context of words. Recent improvements have increased accuracy rates and compatibility. Current popular software options include Dragon NaturallySpeaking, Philips FreeSpeech, IBM ViaVoice, and Lernout & Hauspie Voice Xpress, with Dragon generally performing best according to reviews. The future of speech recognition is expected to include more seamless speech-based user interfaces and command/control capabilities.
Report
Share
Report
Share
![Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Speech Recognition in Artificail Inteligence
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice commands. It works by converting analog audio to digital signals, separating speech from background noise, and analyzing phonetic patterns to recognize words. There are two main types - speaker-dependent software requires training a user's voice, while speaker-independent software can recognize any voice without training but is generally less accurate. Speech recognition has applications in fields like military operations, navigation systems, radiology, and call centers. It offers advantages for people with disabilities but also faces challenges from variations in human speech and filtering noise. The technology continues to improve with advances in processing power and algorithms.
Voice recognition system
The document discusses voice recognition using MatLab. It introduces voice recognition as the process of converting acoustic signals to words. Voice recognition can be used for transcription, command and control, and information access. It discusses the principles and methods of voice recognition, including text-dependent and text-independent approaches. The document outlines the key components of a voice recognition system, including feature extraction using mel-frequency cepstrum coefficients (MFCC), recognition models, and applications like device control and mobile phones. It also reviews the advantages, limitations, and future of voice recognition technology.
Abstract of speech recognition
This document discusses speech recognition technology. It begins by defining speech recognition as the process of converting spoken words to text. It then discusses some key companies in the space, including Nuance Communications which was founded in 1994 as a spinoff from SRI to commercialize speech recognition technology. The document also outlines some features and applications of Dragon speech recognition software, as well as limitations, opportunities, and the future of speech recognition technology.
Speech recognition challenges
Speech recognition systems face challenges related to accuracy, responsiveness, performance, reliability, and fault tolerance. Accuracy can be impacted by audio quality and talk-over problems, which require solutions like echo cancellation and barge-in. Responsiveness requires delayed recognition to avoid processing before resources are ready. Performance issues stem from large grammar sizes and collisions, addressed through optimization and special tokens. Reliability over long periods requires automated testing and drive tests. Fault tolerance involves modular system design that allows restarting components without affecting others.
Speech recognition final presentation
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Speech recognition system seminar
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Noise Adaptive Training for Robust Automatic Speech Recognition
This document provides information about speech recognition software and its potential benefits for students. It discusses how speech recognition works by translating spoken words to text. Using dictation could improve writing for students with learning disabilities by removing barriers from mechanical issues. Speech recognition may support writing quality by removing motor demands and increasing productivity. It also allows access for students unable to use a keyboard. While consistency is important, speech does not need to be perfect. Speech recognition requires cognitive skills and works best for students reading at a third grade level or higher. The document reviews various speech recognition programs and considerations for training and use.
Speech recognition an overview
This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
Recommended
Speech Recognition in Artificail Inteligence
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice commands. It works by converting analog audio to digital signals, separating speech from background noise, and analyzing phonetic patterns to recognize words. There are two main types - speaker-dependent software requires training a user's voice, while speaker-independent software can recognize any voice without training but is generally less accurate. Speech recognition has applications in fields like military operations, navigation systems, radiology, and call centers. It offers advantages for people with disabilities but also faces challenges from variations in human speech and filtering noise. The technology continues to improve with advances in processing power and algorithms.
Voice recognition system
The document discusses voice recognition using MatLab. It introduces voice recognition as the process of converting acoustic signals to words. Voice recognition can be used for transcription, command and control, and information access. It discusses the principles and methods of voice recognition, including text-dependent and text-independent approaches. The document outlines the key components of a voice recognition system, including feature extraction using mel-frequency cepstrum coefficients (MFCC), recognition models, and applications like device control and mobile phones. It also reviews the advantages, limitations, and future of voice recognition technology.
Abstract of speech recognition
This document discusses speech recognition technology. It begins by defining speech recognition as the process of converting spoken words to text. It then discusses some key companies in the space, including Nuance Communications which was founded in 1994 as a spinoff from SRI to commercialize speech recognition technology. The document also outlines some features and applications of Dragon speech recognition software, as well as limitations, opportunities, and the future of speech recognition technology.
Speech recognition challenges
Speech recognition systems face challenges related to accuracy, responsiveness, performance, reliability, and fault tolerance. Accuracy can be impacted by audio quality and talk-over problems, which require solutions like echo cancellation and barge-in. Responsiveness requires delayed recognition to avoid processing before resources are ready. Performance issues stem from large grammar sizes and collisions, addressed through optimization and special tokens. Reliability over long periods requires automated testing and drive tests. Fault tolerance involves modular system design that allows restarting components without affecting others.
Speech recognition final presentation
This is a ppt on speech recognition system or automated speech recognition system. I hope that it would be helpful for all the people searching for a presentation on this technology
Speech recognition system seminar
Speech recognition systems convert spoken words to text in real-time. They are used in dictation software and intelligent assistants. Design challenges include background noise, accent variations, and speed of speech. Speaker dependent systems recognize one voice, while speaker independent systems recognize any voice without training. Speech is broken into phonemes and a hidden Markov model identifies phonemes and language models recognize words. Components include signal analysis, acoustic and language models. Applications include healthcare, military, phones, and personal computers. Siri and Google Now are examples of intelligent assistants using these techniques.
Noise Adaptive Training for Robust Automatic Speech Recognition
This document provides information about speech recognition software and its potential benefits for students. It discusses how speech recognition works by translating spoken words to text. Using dictation could improve writing for students with learning disabilities by removing barriers from mechanical issues. Speech recognition may support writing quality by removing motor demands and increasing productivity. It also allows access for students unable to use a keyboard. While consistency is important, speech does not need to be perfect. Speech recognition requires cognitive skills and works best for students reading at a third grade level or higher. The document reviews various speech recognition programs and considerations for training and use.
Speech recognition an overview
This document provides an overview of speech recognition technology. It defines key terms like utterances, pronunciation, and grammar. It describes how speech recognition works by explaining the acoustic model, grammar, and recognized text. It also discusses standards, performance measurement, and provides an example of Google Search by Voice.
Speech recognition An overview
This document provides an overview of speech recognition technology. It defines speech recognition as the ability to translate spoken words to text. The key components of a speech recognition system include an audio input, grammar, speech recognition engine, acoustic model, and recognized text output. The speech recognition engine uses the acoustic model and grammar to analyze the audio input and return recognized text. Applications of speech recognition include dictation, data entry, and assisting individuals with disabilities. While speech recognition technology has advanced, challenges remain around digitization of speech, signal processing, and accurately recognizing different speakers. The future of assistive technology using speech recognition in education looks promising.
Voice Recognition
The document discusses voice recognition systems and their key components. It describes:
1) Sphinx, an open source tool used for speech recognition that uses Hidden Markov Models and applies feature extraction, language modeling, and acoustic modeling.
2) The CMU lexical access system which hypothesizes words from a phonetic dictionary using syllable anchors.
3) Key parts of speech recognition systems including feature extraction, acoustic modeling, language modeling, and the use of HMMs to match features to models.
Automatic Speech Recognion
The document discusses automatic speech recognition (ASR) and its components. It covers two main approaches to ASR - template matching and feature analysis. Template matching uses pre-recorded templates to match voices, while feature analysis processes voices into features using techniques like linear predictive coding to determine similarities between expected and actual input. The document also describes the process of speech recognition including acoustic wave form analysis, feature extraction, and matching phonemes, words and sentences.
Speech Recognition Technology
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Speech Recognition System
This power-point presentation contains 45 slides. It describes SR system (a brief intro), what are the applications, the biological architecture of human speech recognition vs machine architecture, recognition process, flow summery of recognition process and the approaches to the SRS. All this is described in the first few slides (the first part, let's say), after that, this presentation describes the evolution process of SRS through the decades (the middle part), and at the last this presentation describes the machine learning approach in SRS. How neural net enhance the efficiency of a SRS.
Speech Recognition
Hugo Moreno discusses speech recognition and its applications in control. Speech recognition is the process of converting speech signals to sequences of words through computer algorithms. It involves feature extraction from speech and matching patterns to vocabularies. Speech recognition can be used for applications like elevator control, robot control, translation, stress monitoring, and hands-free computing. It provides an acceptable level of accuracy but improving accuracy reduces speed. Speech recognition involves matching voice patterns to acquire or provide vocabularies.
Group 2 -innovation in smartphones-
Voice recognition software allows users to control their smartphones using voice commands instead of the touchscreen. Popular examples are Siri and Google Now. The document discusses the common functions of Siri and Google Now such as making calls, sending messages, setting reminders and alarms. It then compares the two in areas like voice recognition, speed, searching capabilities, and music identification. Both have advantages like convenience and saving time, as well as disadvantages like inability to always recognize commands accurately. A survey is presented that gauges user experiences with voice recognition software and their views on advantages and disadvantages.
Speech Recognition Technology
Speech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words.
Speech Recognition
This document discusses speech recognition techniques. It begins by defining biometrics and how speech can be used as a biometric for identity authentication. It describes how speech recognition aims to extract lexical information independently of the speaker, while speaker recognition focuses on extracting the identity of the speaker. The document then discusses feature extraction using MFCC and modeling speech using neural networks. It provides an overview of pattern recognition techniques including statistical and structural approaches. Finally, it discusses implementation details such as preprocessing, framing, windowing and feature extraction of speech signals.
Deep Learning For Speech Recognition
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
Automatic speech recognition system
The document summarizes a presentation on automatic speech recognition systems. It includes an introduction defining ASR as the transcription of spoken language into text in real time. It shows the basic block diagram of an ASR system and explains how it works similarly to the human process of hearing, transmitting signals to the brain, and understanding. Some key uses of ASR are in smartphones, AI robots, home automation, and computers. The benefits mentioned are hands-free use, aiding reading and spelling, and easy operation.
Speech Recognition by Iqbal
This report provides an overview of speech recognition technology, including how speech recognition systems work, common applications, and future uses. It discusses key concepts such as utterances, pronunciation, grammar, accuracy, and training. The report also examines speech recognition software and provides examples of free and commercial speech recognition programs. Overall, the report finds that speech recognition has various applications in fields like education, healthcare, gaming, and more, and the technology is expected to continue advancing to support additional future applications.
Ece speech-recognition-report
The document is a seminar report on speech recognition that discusses how speech recognition technology works. It explains that speech recognition systems convert spoken words to electrical signals, break the words down into phonemes, and then match the phonemes to character combinations to output text. The document provides background on speech recognition, covering how the human vocal system produces speech sounds and how early systems from the 1960s aimed to recognize speech, though technology is still being improved.
Speech Recognition System By Matlab
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
A seminar report on speech recognition technology
Complete Report on SPEECH RECOGNITION TECHNOLOGY.
it is very useful for final year students in their seminar.
Automatic speech recognition
Automatic speech recognition allows machines to understand and act on spoken language by converting speech to text. It is a difficult task due to challenges like separating speech from background noise, variability between individuals and within a speaker, and interpreting prosodic features of speech. Systems can be speaker-dependent, requiring training for a specific person, or speaker-independent, covering more language but with reduced robustness. Performance is evaluated based on word error rate or how well a system understands tasks in dialogues. Rule-based and probabilistic approaches use knowledge of phonetics, linguistics and context to interpret speech.
Speech recognition techniques
The document discusses acoustic speech recognition techniques. It provides an introduction and historical survey, then describes the general model of ASR which involves feature extraction and hidden Markov models. It notes the development of early systems in the 1950s-1980s and more recent neural network approaches. The document advocates for expanding ASR systems to support local languages to improve human-machine interaction and information access for more people.
Speech recognition system
This document discusses speech recognition systems and their components. It explains that speech recognition systems involve breaking speech into small sound segments called monophones. They also use grammar and vocabulary files which are compiled into dictionary and finite automata files for execution. The document discusses speaker-dependent, speaker-independent, and speaker-adaptive systems. It also describes isolated word, connected word, and continuous speech recognition. Finally, it provides a high-level overview of the speech to text conversion process.
Ai based character recognition and speech synthesis
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
Speech Recognition
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
Speech recognition
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Artificial intelligence Speech recognition system
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
More Related Content
What's hot
Speech recognition An overview
This document provides an overview of speech recognition technology. It defines speech recognition as the ability to translate spoken words to text. The key components of a speech recognition system include an audio input, grammar, speech recognition engine, acoustic model, and recognized text output. The speech recognition engine uses the acoustic model and grammar to analyze the audio input and return recognized text. Applications of speech recognition include dictation, data entry, and assisting individuals with disabilities. While speech recognition technology has advanced, challenges remain around digitization of speech, signal processing, and accurately recognizing different speakers. The future of assistive technology using speech recognition in education looks promising.
Voice Recognition
The document discusses voice recognition systems and their key components. It describes:
1) Sphinx, an open source tool used for speech recognition that uses Hidden Markov Models and applies feature extraction, language modeling, and acoustic modeling.
2) The CMU lexical access system which hypothesizes words from a phonetic dictionary using syllable anchors.
3) Key parts of speech recognition systems including feature extraction, acoustic modeling, language modeling, and the use of HMMs to match features to models.
Automatic Speech Recognion
The document discusses automatic speech recognition (ASR) and its components. It covers two main approaches to ASR - template matching and feature analysis. Template matching uses pre-recorded templates to match voices, while feature analysis processes voices into features using techniques like linear predictive coding to determine similarities between expected and actual input. The document also describes the process of speech recognition including acoustic wave form analysis, feature extraction, and matching phonemes, words and sentences.
Speech Recognition Technology
This presentation was delivered to a "Web Enabled Business" class at Simon Fraser University in Vancouver. The topic is speech recognition technology, and the presentation covers its origins, how it works, issues, latest trends and future opportunities.
Speech Recognition System
This power-point presentation contains 45 slides. It describes SR system (a brief intro), what are the applications, the biological architecture of human speech recognition vs machine architecture, recognition process, flow summery of recognition process and the approaches to the SRS. All this is described in the first few slides (the first part, let's say), after that, this presentation describes the evolution process of SRS through the decades (the middle part), and at the last this presentation describes the machine learning approach in SRS. How neural net enhance the efficiency of a SRS.
Speech Recognition
Hugo Moreno discusses speech recognition and its applications in control. Speech recognition is the process of converting speech signals to sequences of words through computer algorithms. It involves feature extraction from speech and matching patterns to vocabularies. Speech recognition can be used for applications like elevator control, robot control, translation, stress monitoring, and hands-free computing. It provides an acceptable level of accuracy but improving accuracy reduces speed. Speech recognition involves matching voice patterns to acquire or provide vocabularies.
Group 2 -innovation in smartphones-
Voice recognition software allows users to control their smartphones using voice commands instead of the touchscreen. Popular examples are Siri and Google Now. The document discusses the common functions of Siri and Google Now such as making calls, sending messages, setting reminders and alarms. It then compares the two in areas like voice recognition, speed, searching capabilities, and music identification. Both have advantages like convenience and saving time, as well as disadvantages like inability to always recognize commands accurately. A survey is presented that gauges user experiences with voice recognition software and their views on advantages and disadvantages.
Speech Recognition Technology
Speech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words.
Speech Recognition
This document discusses speech recognition techniques. It begins by defining biometrics and how speech can be used as a biometric for identity authentication. It describes how speech recognition aims to extract lexical information independently of the speaker, while speaker recognition focuses on extracting the identity of the speaker. The document then discusses feature extraction using MFCC and modeling speech using neural networks. It provides an overview of pattern recognition techniques including statistical and structural approaches. Finally, it discusses implementation details such as preprocessing, framing, windowing and feature extraction of speech signals.
Deep Learning For Speech Recognition
Deep Learning techniques have enabled exciting novel applications. Recent advances hold lot of promise for speech based applications that include synthesis and recognition. This slideset is a brief overview that presents a few architectures that are the state of the art in contemporary speech research. These slides are brief because most concepts/details were covered using the blackboard in a classroom setting. These slides are meant to supplement the lecture.
Automatic speech recognition system
The document summarizes a presentation on automatic speech recognition systems. It includes an introduction defining ASR as the transcription of spoken language into text in real time. It shows the basic block diagram of an ASR system and explains how it works similarly to the human process of hearing, transmitting signals to the brain, and understanding. Some key uses of ASR are in smartphones, AI robots, home automation, and computers. The benefits mentioned are hands-free use, aiding reading and spelling, and easy operation.
Speech Recognition by Iqbal
This report provides an overview of speech recognition technology, including how speech recognition systems work, common applications, and future uses. It discusses key concepts such as utterances, pronunciation, grammar, accuracy, and training. The report also examines speech recognition software and provides examples of free and commercial speech recognition programs. Overall, the report finds that speech recognition has various applications in fields like education, healthcare, gaming, and more, and the technology is expected to continue advancing to support additional future applications.
Ece speech-recognition-report
The document is a seminar report on speech recognition that discusses how speech recognition technology works. It explains that speech recognition systems convert spoken words to electrical signals, break the words down into phonemes, and then match the phonemes to character combinations to output text. The document provides background on speech recognition, covering how the human vocal system produces speech sounds and how early systems from the 1960s aimed to recognize speech, though technology is still being improved.
Speech Recognition System By Matlab
This document summarizes a speech recognition system (SRS). SRS uses speech identification and verification. Speech identification determines which registered speaker provided an utterance by extracting features like mel-frequency cepstrum coefficients and comparing them. Speech verification accepts or rejects an identity claim by clustering training vectors from an enrollment session into speaker-specific codebooks using vector quantization. Applications of SRS include banking by phone, voice dialing, voice mail, and security control.
A seminar report on speech recognition technology
Complete Report on SPEECH RECOGNITION TECHNOLOGY.
it is very useful for final year students in their seminar.
Automatic speech recognition
Automatic speech recognition allows machines to understand and act on spoken language by converting speech to text. It is a difficult task due to challenges like separating speech from background noise, variability between individuals and within a speaker, and interpreting prosodic features of speech. Systems can be speaker-dependent, requiring training for a specific person, or speaker-independent, covering more language but with reduced robustness. Performance is evaluated based on word error rate or how well a system understands tasks in dialogues. Rule-based and probabilistic approaches use knowledge of phonetics, linguistics and context to interpret speech.
Speech recognition techniques
The document discusses acoustic speech recognition techniques. It provides an introduction and historical survey, then describes the general model of ASR which involves feature extraction and hidden Markov models. It notes the development of early systems in the 1950s-1980s and more recent neural network approaches. The document advocates for expanding ASR systems to support local languages to improve human-machine interaction and information access for more people.
Speech recognition system
This document discusses speech recognition systems and their components. It explains that speech recognition systems involve breaking speech into small sound segments called monophones. They also use grammar and vocabulary files which are compiled into dictionary and finite automata files for execution. The document discusses speaker-dependent, speaker-independent, and speaker-adaptive systems. It also describes isolated word, connected word, and continuous speech recognition. Finally, it provides a high-level overview of the speech to text conversion process.
Ai based character recognition and speech synthesis
The document discusses an AI seminar on character recognition and speech synthesis. It describes how optical character recognition can convert scanned images or text into machine code, and speech synthesis can artificially produce human speech. It provides details on preprocessing techniques for character recognition, such as de-noising and binarization of images. It also explains the processes of text analysis, phoneme generation and prosody generation used in speech synthesis engines.
Speech Recognition
Speech recognition technology allows users to communicate through spoken commands. It works by converting acoustic speech signals captured by a microphone into text. There are two main types of speech models - speaker independent models that can recognize many people, and speaker dependent models customized for a single person. The speech recognition process involves an audio input being digitized, then broken down into phonemes which are statistically modeled and matched to words in a grammar according to a dictionary to output recognized text.
What's hot (20)
Ai based character recognition and speech synthesis
Ai based character recognition and speech synthesis
Viewers also liked
Speech recognition
Speech recognition, also known as automatic speech recognition or computer speech recognition, allows computers to understand human voice. It has various applications such as dictation, system control/navigation, and commercial/industrial uses. The process involves converting analog audio of speech into digital format, then using acoustic and language models to analyze the speech and output text. There are two main types: speaker-dependent which requires training a model for each user, and speaker-independent which can recognize any voice without training. Accuracy is improving over time as technology advances.
Artificial intelligence Speech recognition system
Speech recognition, also known as automatic speech recognition, allows a computer to understand human voice and perform tasks. It uses acoustic and language models to recognize speech. Acoustic models are statistical representations of sounds created from audio recordings and transcriptions, while language models predict word sequences. There are two main types: speaker-dependent systems require user training to recognize individual voices more accurately, while speaker-independent systems used in applications like phones do not require training but are generally less accurate. The speech recognition process involves digitizing speech, analyzing acoustic signals, and linguistically interpreting the speech to recognize words.
Biometric's final ppt
The document defines biometrics as the automatic identification of a person based on physiological or behavioral characteristics. It lists different biometric characteristics including fingerprint, facial recognition, hand geometry, iris scan, and retina scan. It then describes several biometric recognition techniques such as fingerprint recognition, facial recognition, hand geometry, iris recognition, and retina recognition. Finally, it discusses applications of biometrics such as preventing unauthorized access, criminal identification, and improving security in areas like ATMs, cellphones, computers, automobiles, and airports.
Automatic speech recognition
A basic introduction to Isolated word speech recognition engine for low cost applications.
Credits to respective owners.
Face recognition ppt
This document provides an overview of facial recognition technology. It discusses the history of facial recognition, how the technology works by detecting nodal points on faces and creating faceprints for identification. It also covers implementations, comparing images to templates to verify or identify individuals, and applications in security and surveillance. Strengths are its non-invasive nature, but it can be impacted by changes in appearance.
Hype vs. Reality: The AI Explainer
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Identity authentication using voice biometrics technique
Abstract
Identification of people using name, appearances, badges, tags and register may be effective may be in a small organization.
However, as the size of the organization or society increases, these simple ways of identifying individual become ineffective.
Therefore, it may be necessary to employ additional and more sophisticated means of authenticating the identity of people as the
population increases. Voice Biometrics is a method by which individuals can be uniquely identified by evaluating one or more
distinguishing biological traits associated with the voice of such individuals. In this paper, an unconstrained text-independent
recognition system using the Gaussian Mixture Model was applied to match recorded voice to stored voice for the purpose of
identification of individual. Recorded voices were processed and stored in the enrollment phase while probing voices were used
for comparison in the verification/recognition phase of the system.
Keywords: Model, Biometric, verification, enrollment, database, authentication, matching, identity.
RaspberryPiで作るガイガーカウンター
Raspberry Pi を使って、ガイガーカウンターを作ったことについて紹介した資料。
IoTLT (IoT Lightning Talk) 仙台のイベントで発表のために用意した。
所感のページはイベント終了後に追加した。
Arya.ai artificial intelligence platform vinay kumar
Arya.ai is a developer platform for building deep learning systems. It provides pre-trained models and APIs across computer vision, natural language processing, and other domains to help developers build intelligent applications. The platform handles complex tasks like data processing, model training, and deployment so developers can focus on their products. Arya.ai aims to simplify AI development and accelerate the adoption of intelligent technologies.
Reuse Plastic Bottles.
Plastic bottles are ubiquitous but have disadvantages like being difficult to decompose and recycle. However, there are many ways we can reuse plastic bottles to minimize waste and pollution, such as:
1) Using plastic bottles for drip irrigation of plants to avoid runoff and allow deep watering.
2) Crafting pen holders, coin purses, and flower wall hangings from cut and decorated plastic bottles.
3) Filling bottles with leftover candle wax to create portable lights for outdoor use.
4) Growing food vertically in plastic bottles hung on walls or fences to save space in gardens.
Reusing plastic bottles through these methods helps beautify homes and gardens while reducing environmental impacts
Voice recognition
This document provides an overview of voice recognition biometrics. It discusses the history and development of voice recognition technology from early systems in the 1920s through current applications. The document explains how voice recognition works, capturing a voice sample, creating a voiceprint, and verifying a voice during the authentication process. It highlights benefits of voice recognition systems for security and cost savings but also challenges, such as variations in human voices and environmental noises. Current applications discussed include building access security, corrections monitoring, and telephone banking/ATM verification. The document concludes that voice recognition provides strong security when combined with other authentication methods and will likely continue growing as a biometric technology.
Writing Alexa Voice Skills with NodeJS- David Janes
This document provides an overview of creating Alexa voice skills with Node.js and connecting them to IoT devices. It discusses choosing an architecture, creating a skill in the Alexa Skill Console, defining the interaction model with intents, slots and utterances, testing your skill, and connecting it to IoT devices through a cloud server. Sample code is provided on GitHub to demonstrate handling requests from Alexa and responding with text.
Ear Biometrics
The document discusses ear biometrics for human identification based on image analysis. It provides an introduction to traditional and biometric methods of human identification. Ear biometrics are proposed as a passive and accurate physiological method of identification. Existing ear biometric methods are described that use geometric distances, force field transformations, and principal component analysis. The document then outlines the author's approach using geometric feature extraction from ear contours and classification for identification. Current work involves improving algorithms using additional segmentation and contour-based geometric features.
Voice Recognition Car
This is my Electronics & Communication Engineering Final Year Project PPT. If any one need information then e-mail me to ravilife4rohit@yahoo.com.
Online signature recognition
This document discusses signature recognition as a biometric for identity verification. It defines offline and online signatures, and describes how pen tablets digitize dynamic signatures. The document outlines the key steps of signature recognition systems, including pre-processing, feature extraction of local and global traits, modeling signatures, and detecting forgeries. It notes advantages of signatures being intuitive and independent of language, but also challenges around hardware costs and inconsistent writing abilities.
Voice Identification And Recognition System, Matlab
A simple yet complex approach to modern sophistication.
Made this project using the MFCC approach and then embedding the code to a Graphical User Interface. In the end made a standalone application for the program using deployment tools of matlab
Viewers also liked (18)
Identity authentication using voice biometrics technique
Identity authentication using voice biometrics technique
Arya.ai artificial intelligence platform vinay kumar
Arya.ai artificial intelligence platform vinay kumar
Writing Alexa Voice Skills with NodeJS- David Janes
Writing Alexa Voice Skills with NodeJS- David Janes
Voice Identification And Recognition System, Matlab
Voice Identification And Recognition System, Matlab
Similar to Voice Recognition
Speech recognition1
Speech recognition technology allows users to control systems and enter text using their voice. It works by converting voice input to digital signals, analyzing the sounds using acoustic and language models, and outputting text. Current software provides over 95% accuracy for dictation with training and has improved at system commands. Further advances are still needed to achieve greater accuracy and compatibility across different applications.
Assign
This presentation provides an overview of speech recognition, including its principles, types, key terms, and applications. It discusses how speech recognition works by breaking sounds down into phonemes and using acoustic and language models to match speech to words. Challenges include dealing with noise, recognizing continuous speech, accommodating different accents, and recognizing quickly spoken words. Future improvements may include greater accuracy, context awareness to predict intent, and adaptive microphones.
Speech recognizers & generators
Speech recognition systems translate spoken words to text. They have evolved from discrete dictation to continuous dictation and have gotten smarter with grammar rules. Accuracy can be measured to examine a recognizer's ability. Some systems require training to a specific speaker while others are speaker independent. Computers do speech recognition by digitizing the audio, analyzing it acoustically and linguistically, and interpreting it based on phonemes and a grammar. Speech recognition has applications in navigation, mobile phones, home automation, education, security, and wearable computers. Generators are programs that create other programs, such as password generators, code generators, and random number generators used for licensing keys or testing.
Speech Recognition Technology
Complete power point presentation on SPEECH RECOGNITION TECHNOLOGY.
Very helpful for final year students for their seminar.
One can use this presentation as their final year seminar.
Speech Recognition is a very interesting topic for seminar.
Speech to text conversion
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models along with hidden Markov models to recognize speech and convert it to text. Accuracy can vary based on factors like vocabulary size, speaker dependence, and speech type, but the technology has applications in fields like car systems, healthcare, education and more.
Speech to text conversion
Our speech to text conversion project aims to help the nearly 20% of people worldwide with disabilities by allowing them to control their computer and share information using only their voice. The system uses acoustic and language models with a speech engine to recognize speech and convert it to text. It can perform operations like opening calculator and wordpad. Speech recognition has applications in areas like cars, healthcare, education and daily life. Accuracy depends on factors like vocabulary size, speaker dependence, and speech type (isolated, continuous). The system aims to improve accessibility while reducing costs.
Paper on Speech Recognition
The document discusses a proposed customized speech recognition system that can recognize any regional language. It does this by using Microsoft SAPI to convert words in regional languages to phonemes and store them in a custom grammar database along with their associated actions. During use, a user's spoken words are converted to phonemes using SAPI and compared to the custom grammar database to identify the associated action to perform. This allows the system to recognize and respond to voice commands in any language by training itself on a user's specific regional language.
Speech recognition - how does it work?
Speech recognition works by recording a voice request, removing noise, and dividing the recording into frames. Each frame is analyzed using acoustic and language models to map sounds to phonemes and determine probable word sequences. The most likely sequence is output. Modern systems use machine learning to analyze context and achieve over 90% accuracy, recognizing various accents. While systems from Google, Yandex, and Microsoft use different approaches, all three major players have achieved high recognition quality for their supported languages.
Seminar
The document provides an overview of automatic speech recognition, including: describing the process of speech recognition which involves feature extraction from voice and using acoustic and language models; listing common types like speaker-dependent and independent; discussing applications in areas like dictation, in-car systems, and voice security; and noting both advantages like reducing errors but also challenges involving filtering noise and accommodating various speaking styles.
Web AI.pptx
This document discusses applications of artificial intelligence technologies in web services. It provides examples of speech recognition, which converts speech to text, and speech synthesis, which converts text to speech. For speech recognition, it explains the stages of acoustics, language processing, and implementation. For speech synthesis, it outlines the process of converting text to words, words to phonemes, and generating sounds from phonemes. Overall, the document explores how AI can make web services more user-friendly and responsive through technologies like speech and language processing.
Speech Recognition
Speech recognition converts spoken words to text through techniques like cepstral analysis and hidden Markov models. It has applications in voice commands, data entry, document preparation, translation, and more. Speech recognition systems work by extracting features from audio then using probabilistic models to find the most likely sequence of words given the input sounds. Dialog systems play a key role in conversational interfaces like home appliance control by understanding speech, maintaining discourse context, and determining appropriate system responses.
Beekman5 std ppt_04
The document discusses different types of software including system software that connects hardware and software, applications for consumers and specific industries, and how user interfaces have evolved from character-based to graphical interfaces. It also covers topics like what operating systems do, different platforms, and considerations for consumers when purchasing software and computers.
Speech recognition
Speech recognition, also known as automatic speech recognition or speech to text, is the ability to translate spoken words into text. The process works by a microphone converting sound waves into a digital format, which software then analyzes to break down the message and output text on a screen. Speech recognition has many uses and while it allows for faster, hands-free input with better spelling, factors like slang, noise interference, and overlapping speech can impact accuracy. Popular speech recognition programs include Dragon, Siri, Microsoft SYNC, and Mac Speech Dictate.
Speech recognition
Speech recognition, also known as automatic speech recognition or speech to text, is the ability to translate spoken words into text. The process works by converting sound waves from a microphone into a digital format, which software then analyzes to break down the message and output text. While speech recognition allows for faster, hands-free input and can help those with disabilities, there are also limitations around accuracy with factors like slang, noise interference, and overlapping speech. Popular speech recognition programs include Dragon, Siri, Microsoft SYNC, and Mac Speech Dictate.
10 World’s Leading Speech or Voice Recognition Software That Can 3X Your Prod...
Are you looking for voice recognition software that allows you to search, edit, share, and organize your transcripts? Here are 10 voice & speech recognition software.
General Speereo Technology
Speereo Software provides speech recognition technologies including automatic speech recognition (ASR), text-to-speech (TTS), and speech compression algorithms optimized for embedded devices and mobile phones. Their speech recognition engine achieves high accuracy even in noisy environments while requiring minimal CPU and memory resources. Speereo also offers a speech development SDK to easily integrate speech capabilities into applications.
Speech Recognition, Text to Speech, and Voice Interfaces
This document discusses speech recognition, text-to-speech, and voice interfaces. It describes speech recognition as allowing a user to speak commands into a device instead of pressing buttons. It then discusses uses of speech recognition for individuals with disabilities, medical transcription, dictation, and accessing menu commands. The document goes on to explain how speech recognition works by converting audio into recognized speech through steps like fast Fourier transforms and matching sounds to entries in a codebook. It also discusses discrete vs continuous speech recognition and the future of assistive technology in schools.
Voice Recognition System using Template Matching
It is easy for human to recognize familiar voice but using computer programs to identify a voice when compared with others is a herculean task. This is due to the problem that is encountered when developing the algorithm to recognize human voice. It is impossible to say a word the same way in two different occasions. Human speech analysis by computer gives different interpretation based on varying speed of speech delivery. This research paper gives detail description of the process behind implementation of an effective voice recognition algorithm. The algorithm utilize discrete Fourier transform to compare the frequency spectra of two voice samples because it remained unchanged as speech is slightly varied. Chebyshev inequality is then used to determine whether the two voices came from the same person. The algorithm is implemented and tested using MATLAB.
AI for voice recognition.pptx
This document provides an overview of a seminar on AI for speech recognition. It includes an introduction to AI and speech recognition, different models for speech recognition including HMM and DTW, applications of speech recognition in various domains, and challenges. The content list covers topics like performance of speech recognition systems, applications, and failures of speech recognition. Statistical models are important for decoding speech accurately. AI is recognized as an efficient method for speech recognition.
Tulsa Techfest 2008 - Creating A Voice User Interface With Speech Server
The document discusses creating voice user interfaces with Microsoft Speech Server 2007. It provides an overview of Speech Server 2007 features like support for VoIP, workflows based on Windows Workflow Foundation, and integrated reporting capabilities. It also covers best practices for developing voice applications, including constraining grammars, avoiding open-ended prompts, and letting callers drive the conversation.
Similar to Voice Recognition (20)
10 World’s Leading Speech or Voice Recognition Software That Can 3X Your Prod...
10 World’s Leading Speech or Voice Recognition Software That Can 3X Your Prod...
Speech Recognition, Text to Speech, and Voice Interfaces
Speech Recognition, Text to Speech, and Voice Interfaces
Tulsa Techfest 2008 - Creating A Voice User Interface With Speech Server
Tulsa Techfest 2008 - Creating A Voice User Interface With Speech Server
Recently uploaded
Uni Systems Copilot event_05062024_C.Vlachos.pdf
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI support
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Recently uploaded (20)
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...
Epistemic Interaction - tuning interfaces to provide information for AI support
Epistemic Interaction - tuning interfaces to provide information for AI support
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Voice Recognition
- 6. Recognition (1) Voice Input Analog to Digital Acoustic Model Language Model Display Speech Engine Feedback
- 15. How well do the work? Training Dictation Correct. App. Integrat. Command - Control Dragon Excellent Excellent Good Good Philips Fair Fair Good Good IBM Excellent Good Good Excellent L & H Good Good Good Good