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 processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
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
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 processing and recognition basic in data miningJimit Rupani
Basic presentation about speech processing
Name of the paper i read is :"An educational platform to demonstrate speech processing techniques on Android based smart phones and tablets" on Elsevier
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
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.
YouTube Link: https://youtu.be/sHeJgKBaiAI
** Python Certification Training: https://www.edureka.co/python **
This Edureka video on 'Speech Recognition in Python' will cover the concepts of speech recognition module in python with a program using speech recognition to translate speech into text. Following are the topics discussed:
How Speech Recognition Works?
How To Install SpeechRecognition In Python?
Working With Microphones
How To Install Pyaudio In Python?
Use case
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.
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.
Presentation regarding development of text-to-speech system for Gujarati. Input would be arbitrary Gujarati unicode text while output would equivalent speech sound.
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.
YouTube Link: https://youtu.be/sHeJgKBaiAI
** Python Certification Training: https://www.edureka.co/python **
This Edureka video on 'Speech Recognition in Python' will cover the concepts of speech recognition module in python with a program using speech recognition to translate speech into text. Following are the topics discussed:
How Speech Recognition Works?
How To Install SpeechRecognition In Python?
Working With Microphones
How To Install Pyaudio In Python?
Use case
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.
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.
Presentation regarding development of text-to-speech system for Gujarati. Input would be arbitrary Gujarati unicode text while output would equivalent speech sound.
It is the basic introduction of how the images will be captured and converted form analog to digital format by using sampling and quantization process and further algorithms will be apply on the digitized image.
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Speech Analysis and synthesis using VocoderIJTET Journal
Abstract— In this paper, I proposed a speech analysis and synthesis using a vocoder. Voice conversion systems do not create new speech signals, but just transform existing one. The proposed speech vocoding is different from speech coding. To analyze the speech signal and represent it with less number of bits, so that bandwidth efficiency can be increased. The Synthesis of speech signal from the received bits of information. In this paper three aspects of analysis have been discussed: pitch refinement, spectral envelope estimation and maximum voiced frequency estimation. A Quasi-harmonic analysis model can be used to implement a pitch refinement algorithm which improves the accuracy of the spectral estimation. Harmonic plus noise model to reconstruct the speech signal from parameter. Finally to achieve the highest possible resynthesis quality using the lowest possible number of bits to transmit the speech signal. Future work aims at incorporating the phase information into the analysis and modeling process and also synthesis these three aspects in different pitch period.
Audio/Speech Signal Analysis for Depressionijsrd.com
The word “depressed†is a common everyday word. People might say "I am depressed" when in fact they mean "I am fed up because I have had a row, or failed an exam, or lost my job", etc. These ups and downs of life are common and normal. Most people recover quite quickly. Depression is identified by different methods. Here we are identified depression by MFCC (Mel Frequency Ceptral Coefficient) method. There are different parameters used for the identification of depressed speech and normal speech, but MFCCs based parameter is the most applicable information then other parameter because depressive speech or audio signal can contain more information in the higher energy bands when compared with normal speech.
Speaker Recognition System using MFCC and Vector Quantization Approachijsrd.com
This paper presents an approach to speaker recognition using frequency spectral information with Mel frequency for the improvement of speech feature representation in a Vector Quantization codebook based recognition approach. The Mel frequency approach extracts the features of the speech signal to get the training and testing vectors. The VQ Codebook approach uses training vectors to form clusters and recognize accurately with the help of LBG algorithm.
Effect of Time Derivatives of MFCC Features on HMM Based Speech Recognition S...IDES Editor
In this paper, improvement of an ASR system for
Hindi language, based on Vector quantized MFCC as feature
vectors and HMM as classifier, is discussed. MFCC features
are usually pre-processed before being used for recognition.
One of these pre-processing is to create delta and delta-delta
coefficients and append them to MFCC to create feature vector.
This paper focuses on all digits in Hindi (Zero to Nine), which
is based on isolated word structure. Performance of the system
is evaluated by accurate Recognition Rate (RR). The effect of
the combination of the Delta MFCC (DMFCC) feature along
with the Delta-Delta MFCC (DDMFCC) feature shows
approximately 2.5% further improvement in the RR, with no
additional computational costs involved. RR of the system for
the speakers involved in the training phase is found to give
better recognition accuracy than that for the speakers who
were not involved in the training phase. Word wise RR is
observed to be good in some digits with distinct phones.
Hindi digits recognition system on speech data collected in different natural...csandit
This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building
acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Automatic speech recognition
1.
2. Automatic speech recognition
What is the task?
What are the main difficulties?
How is it approached?
How good is it?
How much better could it be?
2/34
3. What is the task?
Getting a computer to understand spoken language
By “understand” we might mean
React appropriately
Convert the input speech into another medium, e.g. text
Several variables impinge on this
3/34
4. How do humans do it?
Articulation produces sound
waves which the ear conveys
to the brain for processing
4/34
5. How might computers do it?
Digitization
Acoustic analysis of the speech
signal
Linguistic interpretation
5/34
Acoustic waveform Acoustic signal
Speech recognition
7. What’s hard about that?
Digitization
Converting analogue signal into digital representation
Signal processing
Separating speech from background noise
Phonetics
Variability in human speech
Phonology
Recognizing individual sound distinctions (similar phonemes)
Lexicology and syntax
Disambiguating homophones
Features of continuous speech
Syntax and pragmatics
Interpreting prosodic features
Pragmatics
Filtering of performance errors (disfluencies)
7/34
8. Digitization
Analogue to digital conversion
Sampling and quantizing
Use filters to measure energy levels for various
points on the frequency spectrum
Knowing the relative importance of different
frequency bands (for speech) makes this process
more efficient
E.g. high frequency sounds are less informative, so
can be sampled using a broader bandwidth (log
scale)
8/34
9. Separating speech from background noise
Noise cancelling microphones
Two mics, one facing speaker, the other facing away
Ambient noise is roughly same for both mics
Knowing which bits of the signal relate to speech
Spectrograph analysis
9/34
10. Variability in individuals’ speech
Variation among speakers due to
Vocal range (f0, and pitch range – see later)
Voice quality (growl, whisper, physiological elements
such as nasality, adenoidality, etc)
ACCENT !!! (especially vowel systems, but also
consonants, allophones, etc.)
Variation within speakers due to
Health, emotional state
Ambient conditions
Speech style: formal read vs spontaneous
10/34
11. Speaker-(in)dependent systems
Speaker-dependent systems
Require “training” to “teach” the system your individual
idiosyncracies
The more the merrier, but typically nowadays 5 or 10 minutes is enough
User asked to pronounce some key words which allow computer to infer
details of the user’s accent and voice
Fortunately, languages are generally systematic
More robust
But less convenient
And obviously less portable
Speaker-independent systems
Language coverage is reduced to compensate need to be flexible in
phoneme identification
Clever compromise is to learn on the fly
11/34
12. (Dis)continuous speech
Discontinuous speech much easier to recognize
Single words tend to be pronounced more clearly
Continuous speech involves contextual coarticulation
effects
Weak forms
Assimilation
Contractions
12/34
13. Performance errors
Performance “errors” include
Non-speech sounds
Hesitations
False starts, repetitions
Filtering implies handling at syntactic level or above
Some disfluencies are deliberate and have pragmatic
effect – this is not something we can handle in the
near future
13/34
15. Template-based approach
Store examples of units (words, phonemes), then find
the example that most closely fits the input
Extract features from speech signal, then it’s “just” a
complex similarity matching problem, using solutions
developed for all sorts of applications
OK for discrete utterances, and a single user
15/34
16. Template-based approach
Hard to distinguish very similar templates
And quickly degrades when input differs from
templates
Therefore needs techniques to mitigate this
degradation:
More subtle matching techniques
Multiple templates which are aggregated
Taken together, these suggested …
16/34
18. Statistics-based approach
Collect a large corpus of transcribed speech recordings
Train the computer to learn the correspondences
(“machine learning”)
At run time, apply statistical processes to search
through the space of all possible solutions, and pick
the statistically most likely one
18/34
19. Statistics based approach
Acoustic and Lexical Models
Analyse training data in terms of relevant features
Learn from large amount of data different possibilities
different phone sequences for a given word
different combinations of elements of the speech signal for a
given phone/phoneme
Combine these into a Hidden Markov Model expressing
the probabilities
19/34
23. BLOCK DIAGRAM DESCRIPTION
23/34
Speech Acquisition Unit
•It consists of a microphone to obtain the analog speech signal
•The acquisition unit also consists of an analog to digital converter
Speech Recognition Unit
•This unit is used to recognize the words contained in the input speech
signal.
•The speech recognition is implemented in MATLAB with the help of
•template matching algorithm
Device Control Unit
•This unit consists of a microcontroller, the ATmega32, to control the
various appliances
•The microcontroller is connected to the PC via the PC parallel port
•The microcontroller then reads the input word and controls the device
connected to it accordingly.
25. END-POINT DETECTION
25/34
• The accurate detection of a word's start and end points means that
subsequent processing of the data can be kept to a minimum by
processing only the parts of the input corresponding to speech.
•We will use the endpoint detection algorithm proposed by Rabiner and
Sambur. This algorithm is based on two simple time-domain
measurements of the signal - the energy and the zero crossing rate.
The algorithm should tackle the following cases:-
1. Words which begin with or end with a low energy phoneme
2. Words which end with a nasal
3. Speakers ending words with a trailing off in intensity or short breath
26. Steps for EPD
26/34
•Removal of noise by subtracting the signal values with that of noise
• Word extraction
steps –
1. ITU [Upper energy threshold]
2. ITL [Lower energy threshold]
3. IZCT [Zero crossing rate threshold ]
27. Feature Extraction
Input data to the algorithm is usually too large to be
processed
Input data is highly redundant
Raw analysis requires high computational powers and
large amounts of memory
Thus, removing the redundancies and transforming
the data into a set of features
DCT based Mel Cepstrum
27/34
28. DCT Based MFCC
• Take the Fourier transform of a signal.
• Map the powers of the spectrum obtained above onto
the mel scale, using triangular overlapping windows.
• Take the logs of the powers at each of the mel
frequencies.
• Take the discrete cosine transform of the list of mel log
powers, as if it were a signal.
• The MFCCs are the amplitudes of the resulting
spectrum.
28/34
29. MFCC Computation
As Log Magnitude is real and symmetric IDFT reduces to DCT. The DCT
produces highly un-correlated feature yt
(m)(k). The Zero Order MFCC
coefficient yt
(0)(k) is approximately equal to the Log Energy of the frame.
29/34The number of MFCC co-effecients chosen were 13
31. Dynamic Time Warping and Minimum
Distance Paths measurement
Isolated word recognition:
• Task :
• Want to build an isolated word recogniser
• Method:
1. Record, parameterise and store vocabulary of reference words.
2. Record test word to be recognised and parameterize.
3. Measure distance between test word and each reference word.
4. Choose reference word ‘closest’ to test word.
31/34
32. 32
Words are parameterised on a frame-by-frame basis
Choose frame length, over which speech remains reasonably stationary
Overlap frames e.g. 40ms frames, 10ms frame shift
We want to compare frames of test and reference words
i.e. calculate distances between them
40ms
20m
s
33. 33
• Hard:
Number of frames won’t always correspond
• Easy:
Sum differences between corresponding frames
Calculating Distances
34. 34
• Solution 1: Linear Time Warping
Stretch shorter sound
• Problem?
Some sounds stretch more than others
35. 35
• Solution 2:
Dynamic Time Warping (DTW)
5 3 9 7 3
4 7 4
Test
Reference
Using a dynamic alignment, make most similar frames correspond
Find distances between two utterances using these corresponding frames
37. 37
3 5 1 x 4 x 1 x
7 4 3 x 0 x 3 x
9 3 5 x 2 x 5 x
3 2 1 x 4 x 1 x
5 1 1 x 2 x 1 x
1 2 3
4 7 4
Reference
T
e
s
t
Place distance between frame r
of Test and frame c of Reference
in cell(r,c) of distance matrix
DTW Process
38. Constraints
Global
Endpoint detection
Path should be close to diagonal
Local
Must always travel upwards or eastwards
No jumps
Slope weighting
Consecutive moves upwards/eastwards
38
41. Applications
Medical Transcription
Military
Telephony and other domains
Serving the disabled
Further Applications
• Home automation
• Automobile audio systems
• Telematics
41
43. 43/34
all speakers of the
language including
foreign
application
independent or
adaptive
all styles including
human-human
(unaware)
wherever speech
occurs
2015
vehicle noise radio
cell phones
regional accents
native speakers
competent foreign
speakers
some application–
specific data and one
engineer year
natural human-
machine dialog (user
can adapt)
1995
expert years
to create
app– specific
language
model
speaker independent
and adaptive
normal office
various microphones
telephone
planned speech
1985
NOISE
ENVIRONMENT
SPEECH STYLE
USER
POPULATION
COMPLEXITY
1975
quiet room
fixed high –
quality mic
careful
reading
speaker-dep.
application–
specific
speech and
language
Evolution of ASR