The document discusses automatic speech recognition, including identifying the main challenges such as variability in speech, separating speech from background noise, and disambiguating homophones. It describes early approaches using template matching and rule-based systems, and more modern statistical approaches using machine learning techniques like Hidden Markov Models and n-gram language models trained on large speech corpora. The statistical approaches have proven more successful by learning from data rather than relying on hand-crafted rules.
These slides are basically showing how to proceed in the area of Automatic Speech Recognition. I had implemented this whole project using simulation on JFLAP software.
American Standard Sign Language Representation Using Speech Recognitionpaperpublications3
Abstract: For many deaf people, sign language is the principle means of communication. This increases the isolation of hearing impaired people. This paper presents a system prototype that is able to automatically recognize speech which helps to communicate more effectively with the hearing or speech impaired people. This system recognizes speech signal . Recognized spoken words are represented using American standard sign language via a robotic arm and also on the computer using visual basic .In this project a software package is provided to convert the speech signal, (which does not have any meaning for the deaf and the dumb) into the sign language. The main purpose of this project is to bridge the communication and expression gap between the normal people who cannot understand the sign language, and the deaf and dumb who cannot understand the normal speech.
These slides are basically showing how to proceed in the area of Automatic Speech Recognition. I had implemented this whole project using simulation on JFLAP software.
American Standard Sign Language Representation Using Speech Recognitionpaperpublications3
Abstract: For many deaf people, sign language is the principle means of communication. This increases the isolation of hearing impaired people. This paper presents a system prototype that is able to automatically recognize speech which helps to communicate more effectively with the hearing or speech impaired people. This system recognizes speech signal . Recognized spoken words are represented using American standard sign language via a robotic arm and also on the computer using visual basic .In this project a software package is provided to convert the speech signal, (which does not have any meaning for the deaf and the dumb) into the sign language. The main purpose of this project is to bridge the communication and expression gap between the normal people who cannot understand the sign language, and the deaf and dumb who cannot understand the normal speech.
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.
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.
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
mengalami gangguan kelancaran berbicara (gagap) adalah seseorang yang memiliki
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tersendat-sendat, mengulang-ulang ucapanya, dan mendadak berhenti untuk
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Arif's PhD Defense (Title: Efficient Cloud Application Deployment in Distrib...Arif A.
Arif PhD deals with how efficiently applications are deployed in distributed fog environments. A short abstract of the thesis is given below:
Fog computing architectures are composed of a large number of machines distributed across a geographical area such as a city or a region. In this context it is important to support a quick startup of applications deployed in the for of docker containers. This thesis explores the reasons for slow deployment and identifies three improvement opportunities: (1) improving the Docker cache hit rate; (2) speed-up the image installation operation; and (3) accelerate the application boot phase after the creation of a container.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
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Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
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1. Automatic Speech
Recognition
Slides now available at
www.informatics.manchester.ac.uk/~harold/LELA300431/
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 (see
later)
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?
Acoustic waveform Acoustic signal
• Digitization
• Acoustic analysis of the
Speech recognition
speech signal
• Linguistic interpretation
5/34
6. 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)
6/34
7. 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)
7/34
8. 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
8/34
9. 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
9/34
10. 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
10/34
11. Identifying phonemes
• Differences between some phonemes are
sometimes very small
– May be reflected in speech signal (eg vowels
have more or less distinctive f1 and f2)
– Often show up in coarticulation effects
(transition to next sound)
• e.g. aspiration of voiceless stops in English
– Allophonic variation
11/34
12. Disambiguating homophones
• Mostly differences are recognised by humans by
context and need to make sense
It’s hard to wreck a nice beach
What dime’s a neck’s drain to stop port?
• Systems can only recognize words that are in
their lexicon, so limiting the lexicon is an obvious
ploy
• Some ASR systems include a grammar which
can help disambiguation
12/34
13. (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
13/34
14. Interpreting prosodic features
• Pitch, length and loudness are used to
indicate “stress”
• All of these are relative
– On a speaker-by-speaker basis
– And in relation to context
• Pitch and length are phonemic in some
languages
14/34
15. Pitch
• Pitch contour can be extracted from
speech signal
– But pitch differences are relative
– One man’s high is another (wo)man’s low
– Pitch range is variable
• Pitch contributes to intonation
– But has other functions in tone languages
• Intonation can convey meaning
15/34
16. Length
• Length is easy to measure but difficult to
interpret
• Again, length is relative
• It is phonemic in many languages
• Speech rate is not constant – slows down at the
end of a sentence
16/34
17. Loudness
• Loudness is easy to measure but difficult
to interpret
• Again, loudness is relative
17/34
18. 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
18/34
19. Approaches to ASR
• Template matching
• Knowledge-based (or rule-based)
approach
• Statistical approach:
– Noisy channel model + machine learning
19/34
20. 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
20/34
21. 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 …
21/34
22. Rule-based approach
• Use knowledge of phonetics and
linguistics to guide search process
• Templates are replaced by rules
expressing everything (anything) that
might help to decode:
– Phonetics, phonology, phonotactics
– Syntax
– Pragmatics
22/34
23. Rule-based approach
• Typical approach is based on “blackboard”
architecture:
– At each decision point, lay out the possibilities
– Apply rules to determine which sequences are
permitted s
k
i: ʃ
h tʃ
ʃ iə
• Poor performance due to p
t
ɪ h
s
– Difficulty to express rules
– Difficulty to make rules interact
– Difficulty to know how to improve the system
23/34
24. • Identify individual phonemes
• Identify words
• Identify sentence structure and/or meaning
• Interpret prosodic features (pitch, loudness, length)
24/34
25. Statistics-based approach
• Can be seen as extension of template-
based approach, using more powerful
mathematical and statistical tools
• Sometimes seen as “anti-linguistic”
approach
– Fred Jelinek (IBM, 1988): “Every time I fire a
linguist my system improves”
25/34
26. 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
26/34
27. Machine learning
• 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
27/34
29. Language model
• Models likelihood of word given previous
word(s)
• n-gram models:
– Build the model by calculating bigram or
trigram probabilities from text training corpus
– Smoothing issues
29/34
30. The Noisy Channel Model
• Search through space of all possible
sentences
• Pick the one that is most probable given the
waveform
30/34
31. The Noisy Channel Model
• Use the acoustic model to give a set of
likely phone sequences
• Use the lexical and language models to
judge which of these are likely to result in
probable word sequences
• The trick is having sophisticated
algorithms to juggle the statistics
• A bit like the rule-based approach except
that it is all learned automatically from
data
31/34
32. Evaluation
• Funders have been very keen on
competitive quantitative evaluation
• Subjective evaluations are informative, but
not cost-effective
• For transcription tasks, word-error rate is
popular (though can be misleading: all
words are not equally important)
• For task-based dialogues, other measures
of understanding are needed
32/34
33. Comparing ASR systems
• Factors include
– Speaking mode: isolated words vs continuous speech
– Speaking style: read vs spontaneous
– “Enrollment”: speaker (in)dependent
– Vocabulary size (small <20 … large > 20,000)
– Equipment: good quality noise-cancelling mic …
telephone
– Size of training set (if appropriate) or rule set
– Recognition method
33/34
34. Remaining problems
• Robustness – graceful degradation, not catastrophic failure
• Portability – independence of computing platform
• Adaptability – to changing conditions (different mic, background
noise, new speaker, new task domain, new language even)
• Language Modelling – is there a role for linguistics in improving the
language models?
• Confidence Measures – better methods to evaluate the absolute
correctness of hypotheses.
• Out-of-Vocabulary (OOV) Words – Systems must have some
method of detecting OOV words, and dealing with them in a
sensible way.
• Spontaneous Speech – disfluencies (filled pauses, false starts,
hesitations, ungrammatical constructions etc) remain a problem.
• Prosody –Stress, intonation, and rhythm convey important
information for word recognition and the user's intentions (e.g.,
sarcasm, anger)
• Accent, dialect and mixed language – non-native speech is a
huge problem, especially where code-switching is commonplace
34/34