The CUED Speech Group

Dr Mark GalesMachine Intelligence Laboratory

Cambridge University Engineering Department

A. ThermoFluids

B. Electrical Eng

C. Mechanics

D. Structures

E. Management

F. Information Engineering Division

CUED: 6 Divisions 1301100450

Academic StaffUndergradsPostgrads

ControlLab

Signal Processing Lab

Computational and Biological Learning Lab

MachineIntelligence Lab

SpeechGroup

VisionGroup

MedicalImagingGroup

1. CUED Organisation

4 Staff Bill Byrne Mark Gales Phil Woodland Steve Young9 RA’s

12 PhD’s

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2. Speech Group Overview

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• Primary research interests in speech processing– 4 members of Academic Staff– 9 Research Assistants/Associates– 12 PhD students

PhD Projects in Fundamental Speech Technology

Development (10-15 students)

Funded Projects in Recognition/Translation/Synthesis

(5-10 RAs)

MPhil inComputerSpeech,Text and InternetTechnology

ComputerLaboratoryNLIP Group

HTK Software ToolsDevelopment

Computer Speech andLanguage

International Community

Principal Staff and Research Interests

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• Dr Bill Byrne• Statistical machine translation

• Automatic speech recognition

• Cross-lingual adaptation and synthesis

• Dr Mark Gales• Large vocabulary speech recognition

• Speaker and environment adaptation

• Kernel methods for speech processing

• Professor Phil Woodland • Large vocabulary speech recognition/meta-data extraction

• Information retrieval from audio

• ASR and SMT integration

• Professor Steve Young• Statistical dialogue modelling

• Voice conversion

data driven semantic processing statistical modelling

Research Interests

data driven techniques voice transformation HMM-based techniques

large vocabulary systems [Eng, Chinese, Arabic ] acoustic model training and adaptation language model training and adaptation rich text transcription & spoken document retrieval

fundamental theory of statistical modelling and pattern processing

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statistical machine translation finite state transducer framework

Example Current and Recent Projects

• Global Autonomous Language Exploitation – DARPA GALE funded (collab with BBN, LIMSI, ISI …)

• HTK Rich Audio Trancription Project (finished 2004)– DARPA EARS funded

• CLASSIC: Computational Learning in Adaptive Systems for Spoken Conversation

– EU (collab with Edinburgh, France Telecom,,…)

• EMIME: Effective Multilingual Interaction in Mobile Environments- EU (collab with Edinburgh, IDIAP, Nagoya Institute of Technology … )

• R2EAP: Rapid and Reliable Environment Aware Processing- TREL funded

Also active collaborations with IBM, Google, Microsoft, …

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3. Rich Audio Transcription Project

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New algorithms

Natural Speech

Rich Transcript

English/Mandarin

• DARPA-funded project – Effective Affordable Reusable Speech-to-text (EARS) program

• Transform natural speech into human readable form– Need to add meta-data to the ASR output– For example speaker-terms/handle disfluencies

http://mi.eng.cam.ac.uk/research/projects/EARS/index.htmlSee

Rich Text Transcription

okay carl uh do you exercise yeah actually um i belong to a gym down heregold’s gym and uh i try to exercise five days a week um and now and theni’ll i’ll get it interrupted by work or just full of crazy hours you know

ASR Output

Speaker1: / okay carl {F uh} do you exercise /Speaker2: / {DM yeah actually} {F um} i belong to a gym down here / / gold’s gym / / and {F uh} i try to exercise five days a week {F um} / / and now and then [REP i’ll + i’ll] get it interrupted by work or just full of crazy hours {DM you know } /

Meta-Data Extraction (MDE) Markup

Speaker1: Okay Carl do you exercise?Speaker2: I belong to a gym down here, Gold’s Gym, and I try to exercise five days a week and now and then I’ll get it interrupted by work or just full of crazy hours.

Final Text

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4. Statistical Machine Translation

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• Process involves collecting parallel (bitext) corpora– Align at document/sentence/word level

• Use statistical approaches to obtain most probable translation

• Aim is to translate from one language to another– For example translate text from Chinese to English

GALE: Integrated ASR and SMT

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• Member of the AGILE team (lead by BBN)

The DARPA Global Autonomous Language Exploitation (GALE) program has the aim of developing speech and language processing technologies to recognise, analyse, and translate speech and text into readable English.

• Primary languages for STT/SMT: Chinese and Arabic

http://mi.eng.cam.ac.uk/research/projects/AGILE/index.htmlSee

5. Statistical Dialogue Modelling

Speech Understanding

Speech Generation

DialogueManager

System

uS

uA

sA

sS

us SS ,sY

uY

)|( uu YAP

)|( ss AYP

Waveforms Words/Concepts Dialogue Acts

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• Use a statistical framework for all stages

Legend:

ASR: Automatic Speech recognition

NLU: Natural Language Understanding

DM: Dialogue Management

NLG: Natural Language Generation

TTS: Text To Speech

st: Input Sound Signalut: Utterance Hypothesesht: Conceptual Interpretation Hypothesesat: Action Hypotheseswt: Word String Hypothesesrt: Speech Synthesis HypothesesX: possible elimination of hypotheses

CLASSiC: Project Architecture

st

Speech InputASR NLU DM NLG TTS

Context t-1

ut ht atwt

rt

1-Best S

ignal Selection

x

xx

x x

x

Speech output

http://classic-project.orgSee

6. EMIME: Speech-to-Speech Translation

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• Personalised speech-to-speech translation– Learn characteristics of a users speech

– Reproduce users speech in synthesis

• Cross-lingual capability– Map speaker characteristics across languages

• Unified approach for recognition and synthesis– Common statistical model; hidden Markov models

– Simplifies adaptation (common to both synthesis and recognition)

• Improve understanding of recognition/synthesis

http://emime.orgSee

7. R2EAP: Robust Speech Recognition

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• Current ASR performance degrades with changing noise• Major limitation on deploying speech recognition systems

• Aims of the project1. To develop techniques that allow ASR system to rapidly respond to

changing acoustic conditions;

2. While maintaining high levels of recognition accuracy over a wide range of conditions;

3. And be flexible so they are applicable to a wide range of tasks and computational requirements.

• Project started in January 2008 – 3 year duration

• Close collaboration with TREL Cambridge Lab.– Common development code-base – extended HTK

– Common evaluation sets

– Builds on current (and previous) PhD studentships

– Monthly joint meetings

Project Overview

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http://mi.eng.cam.ac.uk/~mjfg/REAP/index.htmlSee

Approach – Model Compensation

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• Model compensation schemes highly effective BUT• Slow compared to feature compensation scheme

• Need schemes to improve speed while maintaining performance• Also automatically detect/track changing noise conditions

• To date 5 Research studentships (partly) funded by Toshiba– Shared software - code transfer both directions

– Shared data sets - both (emotional) synthesis and ASR

– 6 monthly reports and review meetings

• Students and topicsHank Liao (2003-2007): Uncertainty decoding for Noise Robust ASR

Catherine Breslin (2004-2008): Complementary System Generation and Combination

Zeynep Inanoglu (2004-2008): Recognition and Synthesis of Emotion

Rogier van Dalen (2007-2010): Noise Robust ASR

Stuart Moore (2007-2010): Number Sense Disambiguation

• Very useful and successful collaboration

8. Toshiba-CUED PhD Collaborations

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9. HTK Version 3.0 Development

HTK is a free software toolkit for developing HMM-based systems• 1000’s of users worldwide• widely used for research by universities and industry

1989 – 1992

1993 – 1999

2000 – date

V1.0 – 1.4

V1.5 – 2.3

V3.0 – V3.4

Initial development at CUED

Commercial development by Entropic

Academic development at CUED

Development partly funded by Microsoft and DARPA EARS Project

Primary dissemination route for CU research output

http://htk.eng.cam.ac.ukSee

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2004 - date: the ATK Real-time HTK-based recognition system2004 - date: the ATK Real-time HTK-based recognition system

10. Summary

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• Speech Group works on many aspects of speech processing• Large vocabulary speech recognition

• Statistical machine translation

• Statistical dialogue systems

• Speech synthesis and voice conversion

• Statistical machine learning approach to all applications• World-wide reputation for research

• CUED systems have defined state-of-the-art for the past decade

• Developed a number of techniques widely used by industry

• Hidden Markov Model Toolkit (HTK)• Freely-available software, 1000’s of users worldwide

• State-of-the –art features (discriminative training, adaptation …)

• HMM Synthesis extension (HTS) from Nagoya Institute of Technology

http://mi.eng.cam.ac.uk/research/speechSee