Transcript Introduction
Introduction
C.V.
Juan Arturo Nolazco-Flores Associate Professor Computer Science Department, ITESM, campus Monterrey, México Courses: – Speech Processing, Computer Networks.
E-mail: Office: [email protected]
.
Biocomputing Phone: ext. 2726
Plan of Work
Introduction (0.5 hours) Signal Processing and Analysis Methods (1 hours) – Bank-of-filters – Windowing – LPC – Cepstral Coefficients – Vector Quantization
Plan of work
Speech Recognition (6 hours) – HMM Basics (0.5 hours) – Isoltated Word Recognition (1.5 hour) Acoustid Modeling using HMM Evaluation Training D-HMM, CD-HMM Language Model – Continuous Word Recognition Using HMM (1.0 hour) Evaluation Training D-HMM, CD-HMM Language Model
Introduction
What is speech recognition ?
– It is the identification of words in an utterance ( speech-> orthographic transcription ).
– Based on pattern matching techniques.
– Knowledge learn from data, usually using a stochastic techniques.
– It uses powerful algorithms to optimise a mathematical model for a given task.
Notes
Do not confuse with
speech understanding
, which is the identification of the utterance meaning .
Do not confuse with
speaker recognition:
Do not confuse with
speaker identification
, which is the identification of a speaker in a set of speakers.
– Main Problem: The speaker do not want to be identified.
Do not confuse with
speaker verification
, which verifies if a speaker is the one he (she) say he (she) is.
– Main Problem: The speaker can have a pharyngeal problem.
Speech Recongition System Modules
speech
ASR Architecture
text text text text Scoring
Speech Recognition Disciplines
Signal Processing: Spectral analysis.
Physics (Acoustics): Human Hearing studies. Pattern Recognition: Data clustering.
Communication and Information Theory: statistical models, Viterbi algorithms, etc.
Linguistics: grammar and language parsing.
Physiology: knowledge based systems.
Computer Science: efficient algorithms, UNIX, c language.
Task classification
Mode of speaking
Isolated word Connect-word Continuous
Speaker set
Speaker Dependent
Environment
noise free Multi-speaker Independent office telephone
Vocabulary
small (<50) medium (<500) large (<5000) high noise very large (>5000)
History (50’s and 60’s)
Speaker Dependent, – Isolated Digit Recognition System Bell Labs, 1952.
– Phone recogniser (4 vowels and 9 consonants) (UCL, 1959).
Speaker Independent – 10 vowels recognition (MIT, 1959).
Hardware Implementation of small I-SD (60s, Japan).
History
DTW (Variability) and LPC in ASR (70’s).
Connect word Recognition (80’s).
HMMs and Neural Networks in ASR.
Large vocabulary, continuous ASR.
Standard Databases (90’s): – DARPA (Defence Advanced Research Projects Agency) project, 1000-word database.
– World Stree Journal (reading Database) – http://www.ldc.upenn.edu/Catalog/ Spontaneous Speech (90’s)
speech Database text text text text Scoring
Database
Contains – Waveform files store in a specific format (i.e. PCM, micro-law, A-law, GSM).
SPHINX: /net/alf33/usr1/databases/TELEFONICA_1/WAV_FILES – Every waveform files has a Transcription file (either phonemes, words ).
SPHINX: ../../training_input/train.lsn
History (at Present)
Domain Dependent ASR.
Experimentation with new stochastic modelling.
Speech Recognition in Noise.
Speech Recognition for Distorted Speech (Cellular Phones, VoIP).
Experimentation with new way to caracterize the speech.
Why ASR is difficult?
Speech is a complex combination of information from different levels that is used to convey an information.
Signal variability: – Intra-speaker variablity emotional state, environment (Lombard effect) – Inter-speaker variablity physiological differences, accent, dialect, etc.
– Acoustic channel Telephone channel, background noise/speech, etc.
speech Database text text text text Scoring
Acoustic Processing (Speech Processing Front End)
Convert the speech waveform in some type of
parametric
representation.
s k
Speech Processing Front End
Parametric Representation: Zero crossing rate, Short time Energy, Short time
spectral
envelope, etc.
Speech Analysis
What can we observe from this speech waveform:
Speech Signal is non-stationary signal.
Acoustic Processing (Signal Processing Front End)
Convert the speech waveform in some type of
parametric
representation.
s k
Speech Processing Front End
O(t,features)
Time-dependent Parametric Representation.
Examples
Articulation position vs. time Signal Power vs. time (Cohen time-frequency Analysis).
However, trying to obtain the changes in the continuous feature and time spaces is impossible (making some assumptions is possible, but many of the results are not useful for engineering point of view, i.e. Negative Power Spectrums).
Fortunately, if we take small segments of speech, then we can think the speech is stationary in this small segments ( quasi-stationary ).
Short Time Analysis (Discrete time Time-Frequency function)
o
(1)
o
(2)
o
(3)
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(4)
Changing time-resolution
o
(1)
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In speech, normally the size of the segments is between 15 and 25 msec.
The sampling time is 10msec
Acoustic Processing (Signal Processing Front End)
s k
Signal Processing Front End O
=
o
(1)
o
(2)..
o
(T)
Where: o
(t)=[o(t,f_1), o(t,f_2)…o(t,f_P)]
Acoustic Processing (Signal Processing Front End)
s k
Signal Processing Front End Physiological Modelling Processing MFCC Processing LP Cepstra Processing O
=
o
(1)
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(2)..
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(T)
Dynamic Features
In order to incorporate dynamic features of the speech (context information of the speech), the first and/or second derivative can be used.
For example:
c m
k K
K kc m
(
t
k
)
Acoustic Processing (Signal Processing Front End)
s k
Signal Processing Front End O
=
o
(1)
o
(2)..
o
(T)
Where: o
(t)=[o(t,f_1), o(t,f_2)…o(t,f_P), o’(t,f_1), o’(t,f_2)…o’(t,f_P), o”(t,f_1), o”(t,f_2)…o”(t,f_P)]