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
Wreck a nice beach: adventures in speech recognitionStephen Marquard
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
dialogue act modeling for automatic tagging and recognitionVipul Munot
Aim to present comprehensive framework
for modelling and automatic classification of DA’s
founded on well-known statistical methods
Present results obtained with this approach
on large widely available corpus of
spontaneous conversational speech.
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
Wreck a nice beach: adventures in speech recognitionStephen Marquard
Introduction to speech recognition and a description of a project to integrate CMU Sphinx into the Opencast Matterhorn lecture capture system, focusing on language model adaptation using Wikipedia as a corpus.
dialogue act modeling for automatic tagging and recognitionVipul Munot
Aim to present comprehensive framework
for modelling and automatic classification of DA’s
founded on well-known statistical methods
Present results obtained with this approach
on large widely available corpus of
spontaneous conversational speech.
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
Text independent speaker identification system using average pitch and forman...ijitjournal
The aim of this paper is to design a closed-set text-independent Speaker Identification system using average
pitch and speech features from formant analysis. The speech features represented by the speech signal are
potentially characterized by formant analysis (Power Spectral Density). In this paper we have designed two
methods: one for average pitch estimation based on Autocorrelation and other for formant analysis. The
average pitches of speech signals are calculated and employed with formant analysis. From the performance
comparison of the proposed method with some of the existing methods, it is evident that the designed
speaker identification system with the proposed method is superior to others.
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.
Recent advances in LVCSR : A benchmark comparison of performancesIJECEIAES
Large Vocabulary Continuous Speech Recognition (LVCSR), which is characterized by a high variability of the speech, is the most challenging task in automatic speech recognition (ASR). Believing that the evaluation of ASR systems on relevant and common speech corpora is one of the key factors that help accelerating research, we present, in this paper, a benchmark comparison of the performances of the current state-of-the-art LVCSR systems over different speech recognition tasks. Furthermore, we put objectively into evidence the best performing technologies and the best accuracy achieved so far in each task. The benchmarks have shown that the Deep Neural Networks and Convolutional Neural Networks have proven their efficiency on several LVCSR tasks by outperforming the traditional Hidden Markov Models and Guaussian Mixture Models. They have also shown that despite the satisfying performances in some LVCSR tasks, the problem of large-vocabulary speech recognition is far from being solved in some others, where more research efforts are still needed.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
High level speaker specific features modeling in automatic speaker recognitio...IJECEIAES
Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
Speech recognition is the next big step that the technology needs to take for general users. An Automatic Speech Recognition (ASR) will play a major role in focusing new technology to users. Applications of ASR are speech to text conversion, voice input in aircraft, data entry, voice user interfaces such as voice dialing. Speech recognition involves extracting features from the input signal and classifying them to classes using pattern matching model. This can be done using feature extraction method. This paper involves a general study of automatic speech recognition and various methods to generate an ASR system. General techniques that can be used to implement an ASR includes artificial neural networks, Hidden Markov model, acoustic –phonetic approach
Text independent speaker identification system using average pitch and forman...ijitjournal
The aim of this paper is to design a closed-set text-independent Speaker Identification system using average
pitch and speech features from formant analysis. The speech features represented by the speech signal are
potentially characterized by formant analysis (Power Spectral Density). In this paper we have designed two
methods: one for average pitch estimation based on Autocorrelation and other for formant analysis. The
average pitches of speech signals are calculated and employed with formant analysis. From the performance
comparison of the proposed method with some of the existing methods, it is evident that the designed
speaker identification system with the proposed method is superior to others.
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.
Recent advances in LVCSR : A benchmark comparison of performancesIJECEIAES
Large Vocabulary Continuous Speech Recognition (LVCSR), which is characterized by a high variability of the speech, is the most challenging task in automatic speech recognition (ASR). Believing that the evaluation of ASR systems on relevant and common speech corpora is one of the key factors that help accelerating research, we present, in this paper, a benchmark comparison of the performances of the current state-of-the-art LVCSR systems over different speech recognition tasks. Furthermore, we put objectively into evidence the best performing technologies and the best accuracy achieved so far in each task. The benchmarks have shown that the Deep Neural Networks and Convolutional Neural Networks have proven their efficiency on several LVCSR tasks by outperforming the traditional Hidden Markov Models and Guaussian Mixture Models. They have also shown that despite the satisfying performances in some LVCSR tasks, the problem of large-vocabulary speech recognition is far from being solved in some others, where more research efforts are still needed.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
High level speaker specific features modeling in automatic speaker recognitio...IJECEIAES
Spoken words convey several levels of information. At the primary level, the speech conveys words or spoken messages, but at the secondary level, the speech also reveals information about the speakers. This work is based on the high-level speaker-specific features on statistical speaker modeling techniques that express the characteristic sound of the human voice. Using Hidden Markov model (HMM), Gaussian mixture model (GMM), and Linear Discriminant Analysis (LDA) models build Automatic Speaker Recognition (ASR) system that are computational inexpensive can recognize speakers regardless of what is said. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using a standard TIMIT speech corpus. The ASR efficiency of HMM, GMM, and LDA based modeling technique are 98.8%, 99.1%, and 98.6% and Equal Error Rate (EER) is 4.5%, 4.4% and 4.55% respectively. The EER improvement of GMM modeling technique based ASR systemcompared with HMM and LDA is 4.25% and 8.51% respectively.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
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Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2. Outline
Define the problem
What is speech?
Feature Selection
Models
Early methods
Modern statistical models
Current State of ASR
Future Work
3. The ASR Problem
There is no single ASR problem
The problem depends on many factors
Microphone: Close-mic, throat-mic, microphone
array, audio-visual
Sources: band-limited, background noise,
reverberation
Speaker: speaker dependent, speaker
independent
Language: open/closed vocabulary, vocabulary
size, read/spontaneous speech
Output: Transcription, speaker id, keywords
5. What is Speech?
Analog signal produced by humans
You can think about the speech signal being
decomposed into the source and filter
The source is the vocal folds in voiced speech
The filter is the vocal tract and articulators
12. Feature Selection
As in any data-driven task, the data must be
represented in some format
Cepstral features have been found to perform
well
They represent the frequency of the
frequencies
Mel-frequency cepstral coefficients (MFCC)
are the most common variety
13. Where do we stand?
Defined the multiple problems associated with
ASR
Described how speech is produced
Illustrated how speech can be represented in
an ASR system
Now that we have the data, how do we
recognize the speech?
14. Radio Rex
First known attempt at speech recognition
A toy from 1922
Worked by analyzing the signal strength at
500Hz
15. Actual speech recognition
systems
Originally thought to be a relatively simple
task requiring a few years of concerted effort
1969, “Wither speech recognition” is
published
A DARPA project ran from 1971-1976 in
response to the statements in the Pierce
article
We can examine a few general systems
16. Template-Based ASR
Originally only worked for isolated words
Performs best when training and testing
conditions are best
For each word we want to recognize, we
store a template or example based on actual
data
Each test utterance is checked against the
templates to find the best match
Uses the Dynamic Time Warping (DTW)
algorithm
17. Dynamic Time Warping
Create a similarity matrix for the two
utterances
Use dynamic programming to find the lowest
cost path
18. Hearsay-II
One of the systems developed during the
DARPA program
A blackboard-based system utilizing symbolic
problem solvers
Each problem solver was called a knowledge
group
A complex scheduler was used to decide
when each KG should be called
20. DARPA Results
The Hearsay-II system performed much
better than the two other similar competing
systems
However, only one system met the
performance goals of the project
The Harpy system was also a CMU built system
In many ways it was a predecessor to the
modern statistical systems
23. Acoustic Model
For each frame of data, we need some way
of describing the likelihood of it belonging to
any of our classes
Two methods are commonly used
Multilayer perceptron (MLP) gives the likelihood
of a class given the data
Gaussian Mixture Model (GMM) gives the
likelihood of the data given a class
25. Pronunciation Model
While the pronunciation model can be very
complex, it is typically just a dictionary
The dictionary contains the valid
pronunciations for each word
Examples:
Cat: k ae t
Dog: d ao g
Fox: f aa x s
26. Language Model
Now we need some way of representing the
likelihood of any given word sequence
Many methods exist, but ngrams are the
most common
Ngrams models are trained by simply
counting the occurrences of words in a
training set
27. Ngrams
A unigram is the probability of any word in
isolation
A bigram is the probability of a given word
given the previous word
Higher order ngrams continue in a similar
fashion
A backoff probability is used for any unseen
data
28. How do we put it together?
We now have models to represent the three
parts of our equation
We need a framework to join these models
together
The standard framework used is the Hidden
Markov Model (HMM)
29. Markov Model
A state model using the markov property
The markov property states that the future
depends only on the present state
Models the likelihood of transitions between
states in a model
Given the model, we can determine the
likelihood of any sequence of states
30. Hidden Markov Model
Similar to a markov model except the states
are hidden
We now have observations tied to the
individual states
We no longer know the exact state sequence
given the data
Allows for the modeling of an underlying
unobservable process
31. HMMs for ASR
First we build an HMM for each phone
Next we combine the phone models based
on the pronunciation model to create word
level models
Finally, the word level models are combined
based on the language model
We now have a giant network with potentially
thousands or even millions of states
32. Decoding
Decoding happens in the same way as the
previous example
For each time frame we need to maintain two
pieces of information
The likelihood of being at any state
The previous state for every state
33. State of the Art
What works well
Constrained vocabulary systems
Systems adapted to a given speaker
Systems in anechoic environments without
background noise
Systems expecting read speech
What doesn't work
Large unconstrained vocabulary
Noisy environments
Conversational speech
34. Future Work
Better representations of audio based on
humans
Better representation of acoustic elements
based on articulatory phonology
Segmental models that do not rely on the
simple frame-based approach
35. Resources
Hidden Markov Model Toolkit (HTK)
http://htk.eng.cam.ac.uk/
CHIME ( a freely available dataset)
http://spandh.dcs.shef.ac.uk/projects/chime/PCC
/datasets.html
Machine Learning Lectures
http://www.stanford.edu/class/cs229/
http://www.youtube.com/watch?v=UzxYlbK2c7E