Speech Recognition 2003
LIACS Media Lab Leiden University
Seminar
Speech Recognition
2003
E.M. Bakker
LIACS Media Lab
Leiden University
Speech Recognition 2003
LIACS Media Lab Leiden University
Outline
•
Introduction and State of the Art
•
A Speech Recognition Architecture
–
Acoustic modeling
–
Language modeling
–
Practical issues
•
Applications
NB Some of the slides are adapted from the presentation: “
Can Advances in Speech
Recognition make Spoken Language as Convenient and as Accessible as Online Text?”,
an excellent presentation by: Dr. Patti Price, Speech Technology Consulting Menlo Park,
California 94025, and Dr. Joseph Picone Institute for Signal and Information Processing
Dept. of Elect. and Comp. Eng. Mississippi State University
Speech Recognition 2003
LIACS Media Lab Leiden University
Research Areas
•
Speech Analysis
(Production, Perception, Parameter Estimation)
•
Speech Coding/Compression
•
Speech Synthesis
(TTS)
•
Speaker Identification/Recognition/Verification
(Sprint, TI)
•
Language Identification
(Transparent Dialogue)
•
Speech Recognition
(Dragon, IBM, ATT)
Speech recognition sub
-
categories:
•
Discrete/Connected/Continuous Speech/Word Spotting
•
Speaker Dependent/Independent
•
Small/Medium/Large/Unlimited Vocabulary
•
Speaker
-
Independent Large Vocabulary Continuous Speech Recognition (or
LVCSR
for short :)
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction
What is Speech Recognition?
Speech
Recognition
Words
“How are you?”
Speech Signal
Goal:
Automatically extract the string of
words spoken from the speech signal
•
Other interesting area’s:
–
Who is talker (speaker recognition, identification)
–
Speech output (speech synthesis)
–
What the words mean (speech understanding, semantics)
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction
Applications
•
Database query
–
Resource management
–
Air travel information
–
Stock quote
•
Command and control
–
Manufacturing
–
Consumer products
http://www.speech.philips.com
•
Dictation
–
http://www.lhsl.com/contacts/
–
http://www
-
4.ibm.com/software/speech
–
http://www.microsoft.com/speech/
Nuance, American Airlines:
1
-
800
-
433
-
7300, touch 1
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
Speech
-
recognition software
•
IBM (Via Voice, Voice Server Applications,...)
–
Speaker independent, continuous command recognition
–
Large vocabulary recognition
–
Text
-
to
-
speech confirmation
–
Barge in (The ability to interrupt an audio prompt as it is
playing)
•
Dragon Systems
,
Lernout & Hauspie (L&H Voice
Xpress™ (:( )
•
Philips
–
Dictation
–
Telephone
–
Voice Control (SpeechWave, VoCon SDK, chip
-
sets)
•
Microsoft (Whisper, Dr Who)
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
Speech over the telephone.:
•
AT&T Bell Labs
pioneered the use of speech
-
recognition systems for telephone transactions
•
companies such as Nuance, Philips and
SpeechWorks
are active in this field for some
years now.
•
IBM Applications over telephone:
–
request news, internet pages, e
-
mail
–
stock quotes, traveling info
–
weather information
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
Speech over the telephone (Philips):
•
SpeechPearl®
large vocabulary natural language
recognition (up to 200,000 words)
•
SpeechMania®
mixed initiative dialog gives the
caller the impression of a truly natural dialogue:
full replacement of the human operator.
•
SpeechWave™
relatively small vocabularies (up to
hundreds of words) available in nearly 40
languages
•
Voice ReQuest
The system recognizes the request
and routes the call to the appropriate extension,
all without the intervention of an operator.
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
Speech over the telephone. :
•
Brokerage: E*Trade,...
•
Banking
•
Travel: United Airlines,
Continental Airlines
•
BellSouth
•
HP
•
Federal Express
•
Foodline
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
Speech over the telephone.
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
The following is a transcript of a telephone conversation
between Victor Zue and the Jupiter system for weather
information. Jupiter's toll
-
free number is 1
-
888
-
573
-
8255:
•
[Phone ringing]
•
Jupiter
: Hi. Welcome to Jupiter, the M.I.T. Lab for Computer Science
Weather Information System. This call is being recorded for system
development.
You may hang up or ask for help at any time. How can I help you?
•
V.Z.: What is the forecast for Boston today?
•
Jupiter
: In Boston this afternoon, partly sunny and windy. High around 50.
Tonight, clear, lows around 35. What other information can I give you?
•
V.Z.: Will it rain tomorrow?
•
Jupiter
: To my knowledge, the forecast calls for no rain in Boston
tomorrow. Is there anything else?
Speech Recognition 2003
LIACS Media Lab Leiden University
Introduction: State of the Art
•
V.Z.: How about Seattle?
•
Jupiter
: To my knowledge, the forecast calls for
no rain in Seattle tomorrow. Is there something
else you would like to know?
•
V.Z.: Is it sunny anywhere in the Caribbean?
•
Jupiter
: Sorry, I am not sure what you said.
•
V.Z.: Is it sunny anywhere in the Caribbean?
•
Jupiter
: I am sorry, I may not know the city you
are asking about. Try asking for the state or
country. For example, what cities do you know
about in Massachusetts?
•
V.Z.: What is the temperature in Montego Bay?
•
Jupiter
: In Montego Bay today, high 86 and low
73. Is there something else?
•
V.Z.: Good
-
bye.
Speech Recognition 2003
LIACS Media Lab Leiden University
all speakers of
the language
including foreign
application
independent or
adaptive
all styles
including
human
-
human
(unaware)
wherever
speech
occurs
2005
Factors that Affect Performance
of Speech Recognition Systems
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)
2000
expert
years to
create
app
–
specific
language
model
speaker
independent and
adaptive
normal office
various
microphones
telephone
planned
speech
1995
NOISE
ENVIRONMENT
SPEECH STYLE
USER
POPULATION
COMPLEXITY
1985
quiet room
fixed high
–
quality mic
careful
reading
speaker
-
dep.
application
–
specific
speech and
language
Speech Recognition 2003
LIACS Media Lab Leiden University
How Do You Measure the Performance?
USC, October 15, 1999: “the world's first machine system that
can recognize spoken words better than humans can.”
“ In benchmark testing using just a few spoken words, USC's
Berger
-
Liaw … System not only bested all existing
computer speech recognition systems but outperformed
the keenest human ears.”
•
What benchmarks?
•
What was training?
•
What was the test?
•
Were they independent?
•
How large was the vocabulary and the sample size?
•
Did they really test all existing systems?Is that different
from chance?
•
Was the noise added or coincident with speech?
•
What kind of noise? Was it independent of the speech?
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Spontaneous telephone
speech is still a
“grand
challenge”.
•
Telephone
-
quality speech
is still central to
the
problem.
•
Broadcast news is a very
dynamic domain.
0%
10%
30%
40%
20%
Word Error Rate (WER)
Level Of Difficulty
Digits
Continuous
Digits
Command and Control
Letters and Numbers
Broadcast
News
Read Speech
Conversational
Speech
Evaluation Metrics
Speech Recognition 2003
LIACS Media Lab Leiden University
0%
5%
15%
20%
10%
10 dB
16 dB
22 dB
Quiet
Wall Street Journal (Additive Noise)
Machines
Human Listeners (Committee)
Word Error Rate
Speech
-
To
-
Noise Ratio
•
Human performance exceeds machine
performance by a factor ranging from
4x to 10x depending on the task.
•
On some tasks, such as credit card
number recognition, machine
performance exceeds humans due to
human memory retrieval capacity.
•
The nature of the noise is as important
as the SNR (e.g., cellular phones).
•
A primary failure mode for humans is
inattention.
•
A second major failure mode is the lack
of familiarity with the domain (i.e.,
business terms and corporation names).
Evaluation Metrics
Human Performance
Speech Recognition 2003
LIACS Media Lab Leiden University
100%
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
1%
10%
Read
Speech
1k
5k
20k
vocabularies
Noisy
Varied
Microphones
Spontaneous
Speech
Conversational
Speech
Broadcast
Speech
(Foreign)
(Foreign)
10 X
•
A Word Error Rate (WER)
below 10% is considered
acceptable.
•
Performance in the field is
typically 2x to 4x worse than
performance on an evaluation.
Evaluation Metrics
Machine Performance
Speech Recognition 2003
LIACS Media Lab Leiden University
What does a speech signal look like?
Speech Recognition 2003
LIACS Media Lab Leiden University
Spectrogram
Speech Recognition 2003
LIACS Media Lab Leiden University
Speech Recognition
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Measurements of the
signal are ambiguous.
•
Region of overlap represents
classification errors.
•
Reduce overlap by introducing
acoustic and linguistic context
(e.g., context
-
dependent phones).
Feature No. 1
Feature No. 2
Ph_1
Ph_3
Ph_2
•
Comparison of “aa” in “IOck” vs. “iy” in bEAt
for conversational speech (SWB)
Recognition Architectures
Why Is Speech Recognition So Difficult?
Speech Recognition 2003
LIACS Media Lab Leiden University
Overlap in the ceptral space
(alphadigits)
Male “aa”
Male “iy”
Female “aa”
Female “iy”
Speech Recognition 2003
LIACS Media Lab Leiden University
Overlap in the cepstral space
(alphadigits)
Male “aa” (green) vs.
Female “aa” (black)
Male “iy” (blue) vs.
Female “iy” (red)
•
Combined Comparisons:
•
Male "aa" (green)
•
Female "aa" (black)
•
Male "iy" (blue)
•
Female "iy" (red)
Speech Recognition 2003
LIACS Media Lab Leiden University
OVERLAP IN THE CEPSTRAL SPACE
(SWB
-
All)
The following plots demonstrate overlap of recognition features in the cepstral
space. These plots consist of all vowels excised from tokens in the
SWITCHBOARD conversational speech corpus.
All Male Vowels
All Female Vowels
All Vowels
Speech Recognition 2003
LIACS Media Lab Leiden University
Message
Source
Linguistic
Channel
Articulatory
Channel
Acoustic
Channel
Observable: Message
Words
Sounds
Features
Bayesian formulation for speech recognition:
•
P(W|A) = P(A|W) P(W) / P(A)
Recognition Architectures
A Communication Theoretic Approach
Objective: minimize the word error rate
Approach: maximize
P(W|A)
during training
Components:
•
P(A|W)
: acoustic model (hidden Markov models, mixtures)
•
P(W)
: language model (statistical, finite state networks, etc.)
The language model typically predicts a small set of next words based on
knowledge of a finite number of previous words (N
-
grams).
Speech Recognition 2003
LIACS Media Lab Leiden University
Input
Speech
Recognition Architectures
Incorporating Multiple Knowledge Sources
Acoustic
Front
-
end
•
The signal is converted to a sequence of
feature vectors based on spectral and
temporal measurements.
Acoustic Models
P(A/W)
•
Acoustic models represent sub
-
word
units, such as phonemes, as a finite
-
state machine in which states model
spectral structure and transitions
model temporal structure.
Recognized
Utterance
Search
•
Search is crucial to the system, since
many combinations of words must be
investigated to find the most probable
word sequence.
•
The language model predicts the next
set of words, and controls which models
are hypothesized.
Language Model
P(W)
Speech Recognition 2003
LIACS Media Lab Leiden University
Fourier
Transform
Cepstral
Analysis
Perceptual
Weighting
Time
Derivative
Time
Derivative
Energy
+
Mel
-
Spaced Cepstrum
Delta Energy
+
Delta Cepstrum
Delta
-
Delta Energy
+
Delta
-
Delta Cepstrum
Input Speech
•
Incorporate knowledge of the
nature of speech sounds in
measurement of the features.
•
Utilize rudimentary models of
human perception.
Acoustic Modeling
Feature Extraction
•
Typical: 512 samples (16kHz
sampling rate) =>
•
Use a ~30 msec window for
frequency domain analysis.
•
Include absolute energy and
12 spectral measurements.
•
Time derivatives to model
spectral change.
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Acoustic models encode the
temporal evolution of the
features (spectrum).
•
Gaussian mixture distributions
are used to account for
variations in speaker, accent,
and pronunciation.
•
Phonetic model topologies are
simple left
-
to
-
right structures.
•
Skip states (time
-
warping) and
multiple paths (alternate
pronunciations) are also
common features of models.
•
Sharing model parameters is a
common strategy to reduce
complexity.
Acoustic Modeling
Hidden Markov Models
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Word level transcription
•
Supervises a closed
-
loop data
-
driven
modeling
•
Initial parameter estimation
•
The expectation/maximization (EM)
algorithm is used to improve our
parameter estimates.
•
Computationally efficient training
algorithms (Forward
-
Backward) are
crucial.
•
Batch mode parameter updates are
typically preferred.
•
Decision trees and the use of
additional linguistic knowledge are
used to optimize parameter
-
sharing,
and system complexity,.
Acoustic Modeling
Parameter Estimation
•
Initialization
•
Single
Gaussian
Estimation
•
2
-
Way Split
•
Mixture
Distribution
Reestimation
•
4
-
Way Split
•
Reestimation
•••
Speech Recognition 2003
LIACS Media Lab Leiden University
Language Modeling
Is A Lot Like Wheel of Fortune
Speech Recognition 2003
LIACS Media Lab Leiden University
Language Modeling
N
-
Grams: The Good, The Bad, and The Ugly
Bigrams (SWB):
•
Most Common:
“you know”, “yeah SENT!”,
“!SENT um
-
hum”, “I think”
•
Rank
-
100: “do it”, “that we”, “don’t think”
•
Least Common:
“raw fish”, “moisture content”,
“Reagan Bush”
Trigrams (SWB):
•
Most Common:
“!SENT um
-
hum SENT!”,
“a lot of”, “I don’t know”
•
Rank
-
100: “it was a”, “you know that”
•
Least Common:
“you have parents”,
“you seen Brooklyn”
Unigrams (SWB):
•
Most Common: “I”, “and”, “the”, “you”, “a”
•
Rank
-
100: “she”, “an”, “going”
•
Least Common: “Abraham”, “Alastair”, “Acura”
Speech Recognition 2003
LIACS Media Lab Leiden University
Language Modeling
Integration of Natural Language
•
Natural language constraints
can be easily incorporated.
•
Lack of punctuation and search
space size pose problems.
•
Speech recognition typically
produces a word
-
level
time
-
aligned annotation.
•
Time alignments for other levels
of information also available.
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Dynamic programming is used
to find the most probable path
through the network.
•
Beam search is used to
control resources.
Implementation Issues
Dynamic Programming
-
Based Search
•
Search is time synchronous
and left
-
to
-
right.
•
Arbitrary amounts of silence
must be permitted between
each word.
•
Words are hypothesized
many times with different
start/stop times, which
significantly increases
search complexity.
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Cross
-
word Decoding: since word boundaries don’t occur in spontaneous
speech, we must allow for sequences of sounds that span word boundaries.
•
Cross
-
word decoding significantly increases memory requirements.
Implementation Issues
Cross
-
Word Decoding Is Expensive
Speech Recognition 2003
LIACS Media Lab Leiden University
•
Typical LVCSR systems have about 10M free parameters, which makes
training a challenge.
•
Large speech databases are required (several hundred hours of speech).
•
Tying, smoothing, and interpolation are required.
Implementation Issues
Search Is Resource Intensive
Megabytes of Memory
Feature
Extraction
(1M)
Acoustic
Modeling
(10M)
Language
Modeling
(30M)
Search
(150M)
Percentage of CPU
Feature
Extraction
1%
Language
Modeling
15%
Search
25%
Acoustic
Modeling
59%
Speech Recognition 2003
LIACS Media Lab Leiden University
Applications
Conversational Speech
•
Conversational speech collected over the telephone contains background
noise, music, fluctuations in the speech rate, laughter, partial words,
hesitations, mouth noises, etc.
•
WER (Word Error Rate) has decreased from 100% to 30% in six years.
•
Laughter
•
Singing
•
Unintelligible
•
Spoonerism
•
Background Speech
•
No pauses
•
Restarts
•
Vocalized Noise
•
Coinage
Speech Recognition 2003
LIACS Media Lab Leiden University
Applications
Audio Indexing of Broadcast News
Broadcast news offers some unique
challenges:
•
Lexicon: important information in
infrequently occurring words
•
Acoustic Modeling: variations in
channel, particularly within the same
segment (“ in the studio” vs. “on
location”)
•
Language Model: must adapt (“ Bush,”
“Clinton,” “Bush,” “McCain,” “???”)
•
Language: multilingual systems?
language
-
independent acoustic
modeling?
Speech Recognition 2003
LIACS Media Lab Leiden University
Applications
Automatic Phone Centers
•
Portals: Bevocal, TellMe, HeyAniat
•
VoiceXML 2.0
•
Automatic Information Desk
•
Reservation Desk
•
Automatic Help
-
Desk
•
With Speaker identification
•
bank account services
•
e
-
mail services
•
corporate services
Speech Recognition 2003
LIACS Media Lab Leiden University
•
From President Clinton’s State of the Union address (January 27, 2000):
“These kinds of innovations are also propelling our remarkable prosperity...
Soon researchers will bring us devices that can
translate foreign languages
as fast as you can talk
... molecular computers the size of a tear drop with the
power of today’s fastest supercomputers.”
Applications
Real
-
Time Translation
•
Imagine a world where:
•
You book a travel reservation from your cellular phone while driving in
your car without ever talking to a human (
database query
)
•
You converse with someone in a foreign country and neither speaker
speaks a common language (
universal translator
)
•
You place a call to your bank to inquire about your bank account and
never have to remember a password (
transparent telephony
)
•
You can ask questions by voice and your Internet browser returns
answers to your questions (
intelligent query
)
•
Human Language Engineering
: a sophisticated integration of many speech and
language related technologies...
a science for the next millennium
.
Speech Recognition 2003
LIACS Media Lab Leiden University
Conclusions:
•
supervised training is a good
machine learning technique
•
large databases are essential for
the development of robust statistics
Challenges:
•
discrimination vs. representation
•
generalization vs. memorization
•
pronunciation modeling
•
human
-
centered language modeling
The algorithmic issues for the next decade:
•
Better features by extracting articulatory information?
•
Bayesian statistics? Bayesian networks?
•
Decision Trees? Information
-
theoretic measures?
•
Nonlinear dynamics? Chaos?
Technology
Future Directions
1970
Hidden Markov Models
Analog Filter Banks
Dynamic Time
-
Warping
1980
1990
2000
1960
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