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

Seminar

Speech Recognition

2003

E.M. Bakker

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


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 :)


Introduction What is Speech Recognition?

SpeechRecognition

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)


IntroductionApplications

• 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


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 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 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 over the telephone. :

• Brokerage: E*Trade,...

• Banking

• Travel: United Airlines, Continental Airlines

• BellSouth

• HP

• Federal Express

• Foodline



Speech over the telephone.



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?



• 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.


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 radiocell phones

regional accentsnative 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 officevarious microphonestelephone

planned speech

1995

NOISE ENVIRONMENT

SPEECH STYLE

USER

POPULATION

COMPLEXITY

1985

quiet roomfixed high –quality mic

careful reading

speaker-dep.

application– specific speech and language


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?


• 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

ContinuousDigits

Command and Control

Letters and Numbers

BroadcastNews

Read Speech

ConversationalSpeech

Evaluation Metrics


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 MetricsHuman Performance


100%

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 20031%

10%

ReadSpeech

1k5k

20k vocabularies

Noisy

VariedMicrophones

SpontaneousSpeech

ConversationalSpeech

BroadcastSpeech

(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 MetricsMachine Performance


What does a speech signal look like?


Spectrogram


Speech Recognition


• 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_3Ph_2

• Comparison of “aa” in “IOck” vs. “iy” in bEAt for conversational speech (SWB)

Recognition ArchitecturesWhy Is Speech Recognition So Difficult?


Overlap in the ceptral space (alphadigits)

Male “aa” Male “iy”

Female “aa” Female “iy”


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)


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


MessageSource

LinguisticChannel

ArticulatoryChannel

AcousticChannel

Observable: Message Words Sounds Features

Bayesian formulation for speech recognition:

• P(W|A) = P(A|W) P(W) / P(A)

Recognition ArchitecturesA 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).


Input Speech

Recognition ArchitecturesIncorporating Multiple Knowledge Sources

AcousticFront-end

AcousticFront-end

• The signal is converted to a sequence of feature vectors based on spectral and temporal measurements.

Acoustic ModelsP(A/W)

Acoustic ModelsP(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.

RecognizedUtterance

SearchSearch

• 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)


FourierTransform

FourierTransform

CepstralAnalysis

CepstralAnalysis

PerceptualWeighting

PerceptualWeighting

TimeDerivative

TimeDerivative

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 ModelingFeature Extraction

• Typical: 512 samples (16kHz sampling rate) =>

• Use a ~30 msec window forfrequency domain analysis.

• Include absolute energy and 12 spectral measurements.

• Time derivatives to model spectral change.


• 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 ModelingHidden Markov Models


• 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 ModelingParameter Estimation

• Initialization

• Single Gaussian Estimation

• 2-Way Split

• Mixture Distribution Reestimation

• 4-Way Split

• Reestimation

•••


Language ModelingIs A Lot Like Wheel of Fortune


Language ModelingN-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”


Language ModelingIntegration 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.


• Dynamic programming is used to find the most probable path through the network.

• Beam search is used to control resources.

Implementation IssuesDynamic 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.


• 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 IssuesCross-Word Decoding Is Expensive


• 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

FeatureExtraction

(1M)

Acoustic Modeling

(10M)

LanguageModeling (30M)

Search(150M)

Percentage of CPUFeature

Extraction1%

LanguageModeling

15%

Search 25% Acoustic

Modeling 59%


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


ApplicationsAudio Indexing of Broadcast News

Broadcast news offers some uniquechallenges:• 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?


ApplicationsAutomatic 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


• 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 languagesas fast as you can talk... molecular computers the size of a tear drop with thepower 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 speakerspeaks 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.


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 modelingThe 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 ModelsAnalog Filter Banks Dynamic Time-Warping

1980 19902000

1960

Speech Recognition 2003 LIACS Media Lab Leiden University Seminar Speech Recognition 2003 E.M. Bakker LIACS Media Lab Leiden University.

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