AUTOMATIC SPEECH RECOGNITION SYSTEM USING KALDI
•Download as PPTX, PDF•
0 likes•65 views
Automatic Speech Recognition (ASR) is the process of converting an unknown speech waveform into the corresponding orthographic transcription
Report
Share
Report
Share
Recommended
Whitepaper Avira about Artificial Intelligence to cyber security
This document discusses the application of artificial intelligence (AI) and machine learning to cybersecurity. It notes that the rate of malware creation far exceeds what can be addressed manually, so AI is needed to help scale detection. The document outlines different forms of AI, including applied/narrow AI, artificial general intelligence, and strong AI. It focuses on how Avira uses applied machine learning and deep learning techniques as part of its AI platform to augment endpoint security with a cloud-based solution and improve malware detection rates above 99%.
A seminar report on speech recognition technology
Complete Report on SPEECH RECOGNITION TECHNOLOGY.
it is very useful for final year students in their seminar.
Speech Recognition
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
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 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.
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 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.
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
Whitepaper Avira about Artificial Intelligence to cyber security
This document discusses the application of artificial intelligence (AI) and machine learning to cybersecurity. It notes that the rate of malware creation far exceeds what can be addressed manually, so AI is needed to help scale detection. The document outlines different forms of AI, including applied/narrow AI, artificial general intelligence, and strong AI. It focuses on how Avira uses applied machine learning and deep learning techniques as part of its AI platform to augment endpoint security with a cloud-based solution and improve malware detection rates above 99%.
A seminar report on speech recognition technology
Complete Report on SPEECH RECOGNITION TECHNOLOGY.
it is very useful for final year students in their seminar.
Speech Recognition
Also known as automatic speech recognition or computer speech recognition which means understanding voice by the computer and performing any required task.
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 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.
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 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.
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 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.
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.
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.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
This document provides an overview of automatic speech recognition systems. It begins with an introduction that defines automatic speech recognition as the real-time transcription of spoken language into text. It then includes a block diagram showing the main components, and describes the goal of accurately converting speech signals to text independently of speaker or device. Applications discussed include smart phones, artificial intelligence systems, home automation, and computers. The document also covers related technologies, benefits like hands-free use, and concludes that this technology is beneficial for both public and private sectors.
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.
Face recognization
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
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.
Sign language recognition for deaf and dumb people
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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
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.
Language translation english to hindi
This document discusses natural language processing and machine translation. It describes natural language processing as a field concerned with interactions between computers and human languages. It then lists some common NLP applications like question answering, text categorization, and machine translation. The document discusses rule-based and statistical approaches to machine translation and some issues in machine translation like word order differences between languages and ambiguity. It also briefly describes some existing machine translation systems like Systran, Bing Translator, and Google Translator.
Automatic speech recognition system using deep learning
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
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.
Text extraction From Digital image
Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.
Automatic speech recognition
This document discusses automatic speech recognition, including defining the task, the main challenges, and common approaches. The key difficulties identified are digitizing speech, separating speech from noise, dealing with variability between individuals, identifying phonemes, disambiguating homophones, handling continuous speech, and interpreting prosodic features. Common approaches are template matching, rule-based systems, and statistical/machine learning methods like hidden Markov models. Remaining challenges include robustness, adaptability, language modeling, and handling spontaneous speech.
Ocr abstract
The document describes a project to develop optical character recognition (OCR) software for recognizing online and offline handwritten text in multiple languages. It aims to recognize characters from scanned documents or real-time handwriting input and create a user profile. The system scope includes recognizing handwriting from multiple users and cursive script. It will store recognized characters in a text file and optionally convert words to audio for reading documents aloud. The document provides details on OCR technology, applications, literature review, user and system requirements, and the project's goal of using OCR for applications like forms processing.
MIT Project Oxygen - A seminar report
This document provides an overview of Project Oxygen, which aims to embed computation into everyday human life through pervasive and human-centered computing technologies. It describes several key technologies being developed, including intelligent spaces (E21) that can understand speech and gestures, mobile devices (H21) that are lightweight and customizable, and networks (N21) that allow devices to automatically discover and collaborate with each other. The goal is for computation to be seamlessly integrated and accessible anywhere, through technologies like knowledge access and automation. Project Oxygen is moving closer to realizing Mark Weiser's vision of ubiquitous computing.
Speech Recognition
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
CoreML for NLP (Melb Cocoaheads 08/02/2018)
This document provides an overview of using CoreML for natural language processing (NLP) tasks on Android and iOS. It discusses topics like word embeddings, recurrent neural networks, using Keras/Tensorflow models with CoreML, and an automated workflow for training models and deploying them to Android and iOS. It describes using FastText word embeddings to vectorize text, building recurrent neural network models in Keras, converting models to CoreML format, and using Jinja templating to generate code for integrating models into mobile applications. The overall goal is to automatically train NLP models and deploy them to mobile in a way that supports offline usage.
DSpace Under the Hood
-DSpace Under the Hood-
As presented at OR10 in July 2010-
Part 1: How does DSpace work?: Whilst you don't need to be a mechanic to drive a car, it is helpful if you have a basic understanding of how a car works, what bits do different jobs, and how to top up your oil and pump up your tyres / tires. This presentation will give an overview of the DSpace architecture, and will give you enough knowledge to understand how DSpace works. By knowing this, you will also learn about ways DSpace could be used, and ways in which it can't be used.
Part 2: The development process and YOUR role in it:
DSpace development in undertaken by the DSpace community. No one, or no organisation is in charge, and without contributions from the DSpace community the platform would not continue to develop and evolve. Sometimes it can appear that there are people in charge, or that unless you are a technical developer then there is no way or need to contribute. This presentation will explain how DSpace development usually takes place, where and who has input at different stages, and will equip you to contribute further, or to help you contribute for the first time.
Presenters - members of the DSpace Committers and DSpace Global Outreach Committee:
Lewis, Stuart ; Hayes, Leonie ; Stangeland, Elin ; Shepherd, Kim ; Jones, Richard ; Roos, Monica
More Related Content
What's hot
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.
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.
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...Universitat Politècnica de Catalunya
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.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
This document provides an overview of automatic speech recognition systems. It begins with an introduction that defines automatic speech recognition as the real-time transcription of spoken language into text. It then includes a block diagram showing the main components, and describes the goal of accurately converting speech signals to text independently of speaker or device. Applications discussed include smart phones, artificial intelligence systems, home automation, and computers. The document also covers related technologies, benefits like hands-free use, and concludes that this technology is beneficial for both public and private sectors.
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.
Face recognization
This document provides an overview of face recognition technology. It discusses 2D and 3D facial recognition, how the technology works by measuring facial features to create a unique face print, hardware and software requirements, advantages like identifying repeat offenders, and applications in security, multimedia, and law enforcement. The conclusion states that while progress has been made, continued work is needed to develop more accurate systems.
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.
Sign language recognition for deaf and dumb people
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
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
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.
Language translation english to hindi
This document discusses natural language processing and machine translation. It describes natural language processing as a field concerned with interactions between computers and human languages. It then lists some common NLP applications like question answering, text categorization, and machine translation. The document discusses rule-based and statistical approaches to machine translation and some issues in machine translation like word order differences between languages and ambiguity. It also briefly describes some existing machine translation systems like Systran, Bing Translator, and Google Translator.
Automatic speech recognition system using deep learning
This document describes the development of an automatic speech recognition system using deep learning techniques. It discusses extracting MFCC features from audio signals and using a convolutional neural network for feature extraction, followed by a Gaussian mixture model-hidden Markov model for recognition. It also describes implementing a speech recognition system using the Kaldi toolkit on a digits dataset consisting of 10 speakers, as well as an automatic speaker recognition system using MFCC features and K-nearest neighbors classification. The speech recognition system achieved an accuracy of 72% and the speaker recognition system achieved 80% accuracy on the digits dataset.
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.
Text extraction From Digital image
Text Extraction is a process by which we convert Printed document/Scanned Page or Image in which text are available to ASCII Character that a Computer can Recognize.
Automatic speech recognition
This document discusses automatic speech recognition, including defining the task, the main challenges, and common approaches. The key difficulties identified are digitizing speech, separating speech from noise, dealing with variability between individuals, identifying phonemes, disambiguating homophones, handling continuous speech, and interpreting prosodic features. Common approaches are template matching, rule-based systems, and statistical/machine learning methods like hidden Markov models. Remaining challenges include robustness, adaptability, language modeling, and handling spontaneous speech.
Ocr abstract
The document describes a project to develop optical character recognition (OCR) software for recognizing online and offline handwritten text in multiple languages. It aims to recognize characters from scanned documents or real-time handwriting input and create a user profile. The system scope includes recognizing handwriting from multiple users and cursive script. It will store recognized characters in a text file and optionally convert words to audio for reading documents aloud. The document provides details on OCR technology, applications, literature review, user and system requirements, and the project's goal of using OCR for applications like forms processing.
MIT Project Oxygen - A seminar report
This document provides an overview of Project Oxygen, which aims to embed computation into everyday human life through pervasive and human-centered computing technologies. It describes several key technologies being developed, including intelligent spaces (E21) that can understand speech and gestures, mobile devices (H21) that are lightweight and customizable, and networks (N21) that allow devices to automatically discover and collaborate with each other. The goal is for computation to be seamlessly integrated and accessible anywhere, through technologies like knowledge access and automation. Project Oxygen is moving closer to realizing Mark Weiser's vision of ubiquitous computing.
Speech Recognition
speech recognition,History of speech recognition,what is speech recognition,Voice recognition software , Advantages and Disadvantages speech recognition, voice recognition,Voice recognition in operating systems ,Types of speech recognition
What's hot (20)
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...
Speech Recognition with Deep Neural Networks (D3L2 Deep Learning for Speech a...
Sign language recognition for deaf and dumb people
Sign language recognition for deaf and dumb people
Automatic speech recognition system using deep learning
Automatic speech recognition system using deep learning
Similar to AUTOMATIC SPEECH RECOGNITION SYSTEM USING KALDI
CoreML for NLP (Melb Cocoaheads 08/02/2018)
This document provides an overview of using CoreML for natural language processing (NLP) tasks on Android and iOS. It discusses topics like word embeddings, recurrent neural networks, using Keras/Tensorflow models with CoreML, and an automated workflow for training models and deploying them to Android and iOS. It describes using FastText word embeddings to vectorize text, building recurrent neural network models in Keras, converting models to CoreML format, and using Jinja templating to generate code for integrating models into mobile applications. The overall goal is to automatically train NLP models and deploy them to mobile in a way that supports offline usage.
DSpace Under the Hood
-DSpace Under the Hood-
As presented at OR10 in July 2010-
Part 1: How does DSpace work?: Whilst you don't need to be a mechanic to drive a car, it is helpful if you have a basic understanding of how a car works, what bits do different jobs, and how to top up your oil and pump up your tyres / tires. This presentation will give an overview of the DSpace architecture, and will give you enough knowledge to understand how DSpace works. By knowing this, you will also learn about ways DSpace could be used, and ways in which it can't be used.
Part 2: The development process and YOUR role in it:
DSpace development in undertaken by the DSpace community. No one, or no organisation is in charge, and without contributions from the DSpace community the platform would not continue to develop and evolve. Sometimes it can appear that there are people in charge, or that unless you are a technical developer then there is no way or need to contribute. This presentation will explain how DSpace development usually takes place, where and who has input at different stages, and will equip you to contribute further, or to help you contribute for the first time.
Presenters - members of the DSpace Committers and DSpace Global Outreach Committee:
Lewis, Stuart ; Hayes, Leonie ; Stangeland, Elin ; Shepherd, Kim ; Jones, Richard ; Roos, Monica
[C12]元気Hadoop! OracleをHadoopで分析しちゃうぜ by Daisuke Hirama
This document discusses using Hadoop and related tools to analyze performance and audit logs from an Oracle database. It describes:
1. Extracting performance log data from an Oracle database using dstat and loading it into HDFS for analysis with Hadoop and Hive.
2. Using audit logs to find suspicious SQL queries, extracting the queries from XML tags with a Hadoop Streaming script, and classifying queries as normal or suspicious using Mahout machine learning.
3. The document compares the performance of Hive, Impala, and a relational database for analytics workloads, finding that Impala can provide much faster query speeds than Hive for most cases.
useR! 2012 Talk
This document discusses using R to scrape baseball box score data from the website baseball-reference.com. It describes breaking down the scraping process into steps: iterating through each MLB team from 2005-2010, each date from April to October, and accessing the box score URLs and tables. The document outlines the R code used to systematically scrape over 78,000 URLs and extract the data tables. It concludes that R provides a powerful platform for web scraping and accessing large amounts of publicly available data.
サービスをスケールさせるために AWSと利用者の技術
The document discusses Amazon Web Services (AWS) architecture and services that can be used to support live TV and media workflows, including content acquisition, production, playout and distribution, digital asset management, and over-the-top delivery. It provides examples of architectures using AWS services like Amazon S3, EC2, RDS, CloudFront, and others to support functions like encoding, packaging, storage, caching, and delivery of live video streams and video on demand. The document also references AWS regions and availability zones that media companies can leverage around the world for their cloud infrastructure.
Внедрение SDLC в боевых условиях / Егор Карбутов (Digital Security)
РИТ++ 2017, секция ML + IoT + ИБ
Зал Белу-Оризонти, 5 июня, 12:00
Тезисы:
http://ritfest.ru/2017/abstracts/2758.html
Наш доклад на тему, которая практически не имеет подробного описания в интернете. Мы хотим рассказать, как мы (Digital Security) - компания, которая специализируется на анализе защищённости и исследованиях в области ИБ - внедрились в цикл разработки продуктов. Посвятим немного времени SDLC.
Расскажем историю внедрения своей команды для повышения общего уровня безопасности различных аспектов в уже существующий большой проект. Опишем, как строим свои процессы от общего выделения времени, разделения большого количества различных сервисов на компоненты, до отдельных уязвимостей и применяемых нами тулзов.
qdoc.tips_oracle-dba-interview-questions.pdf
This document contains 27 Oracle DBA interview questions and answers covering topics such as:
- Components of the shared pool and how to configure SGA for an OLTP environment
- Using views like v$session and v$process to associate users with OS processes
- Scheduling jobs using DBMS_JOB or CRONTAB to run procedures and scripts
- Diagnosing issues like locking, high buffer waits, database hangs using views like v$lock and alert logs
- Extracting DDL, checking tablespace usage, and recovering from file system corruption
Experiences with Debugging Data Races
With multicore systems becoming the norm, every programmer is being forced to deal with multi-CPU memory atomicity bugs: data races. Data-race bugs are some of the hardest bugs to find and fix, sometimes taking weeks on end, even for experts. There are very few tools to help here (mostly just academic implementations). The authors of this presentation are at the forefront of multicore Java technology-based systems and daily have to debug data races. They have a lot of hard-won experiences with finding and fixing such bugs, and they share them with you in this presentation.
MongoDB for Time Series Data Part 3: Sharding
The document discusses sharding time series sensor data in MongoDB. It recommends modeling the application's read, write and storage patterns to determine the optimal sharding strategy. A good shard key has sufficient cardinality, distributes writes evenly and enables targeted reads. For time series data, a compound shard key of an arbitrary value and incrementing timestamp is suggested to balance hot spots and targeted queries. The document also covers configuring a sharded cluster and replica sets with tags to control data distribution.
Similar to AUTOMATIC SPEECH RECOGNITION SYSTEM USING KALDI (9)
[C12]元気Hadoop! OracleをHadoopで分析しちゃうぜ by Daisuke Hirama
[C12]元気Hadoop! OracleをHadoopで分析しちゃうぜ by Daisuke Hirama
Внедрение SDLC в боевых условиях / Егор Карбутов (Digital Security)
Внедрение SDLC в боевых условиях / Егор Карбутов (Digital Security)
Recently uploaded
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
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.
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.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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
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.
Programming Foundation Models with DSPy - Meetup Slides
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
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
Recently uploaded (20)
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Programming Foundation Models with DSPy - Meetup Slides
Programming Foundation Models with DSPy - Meetup Slides
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
AUTOMATIC SPEECH RECOGNITION SYSTEM USING KALDI
- 1. S U B M I T T E D B Y - P R I Y A N S H U P A L D U T T A ( C S M 2 1 0 1 5 ) S U B H R O J I T S A I K I A ( C S M 2 1 0 0 3 )
- 2. • • •
- 3. Automatic Speech Recognition (ASR) is the process of converting an unknown speech waveform into the corresponding orthographic transcription
- 5. • • • • • •
- 8. • • •
- 9. •
- 10. • Download from the http://github.com/kaldi-asr/kaldi. 2. WE NEED TO INSTALL ALL THE DEPENDENCIES FOR KALDI TO WORK PROPERLY. • • •
- 12. Precondition We have collected some amount of audio data that contain only spoken digits by 9 different speakers. Each audio file is an entire spoken sentence in Assamese (e.g. 'এক',দুই,তিতি,চাতি etc). Purpose We have to divide our data into train and test sets, set up an ASR system, train it, test it and get some decoding results. First task Now we have to create the project in the kaldi/egs/ directory. This is a place where We will put all the stuff related to the project. Our Approach To Build the system
- 14. •
- 16. In order to decode the text we have create two files (for some configuration modifications in decoding and mfcc feature extraction processes ): a.) decode.config b.) mfcc.conf
- 17. Training Method
- 19. outpu t
- 20. THANK YOU