Speaker Recognition Systems - ICSI | ICSI

50 Pages·2011·1.04 MB·English

The 2004 MIT Lincoln Laboratory
Speaker Recognition System

D.A.Reynolds, W. Campbell, T. Gleason, C. Quillen, D.
Sturim, P. Torres-Carrasquillo, A. Adami (ICASSP 2005)

CS298 Seminar
Shaunak Chatterjee
09-23-2011
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Actually …
• Robust text-independent speaker identification using Gaussian mixture speaker
models
– Reynolds, Rose (1995)
• Speaker verification using adapted Gaussian mixture models
– Reynolds, Quatieri, Bunn (2000)
• Speaker recognition based on idiolectal differences between speakers
– Doddington (2001)
• Generalized linear discriminant sequence kernels for speaker recognition
– Campbell (2002)
• Modeling prosodic dynamics for speaker recognition
– Adami, Mihaescu, Reynolds, Godfrey (2003)
• Speaker adaptive cohort selection for Tnorm in text-independent speaker
verification
– Sturim, Reynolds (2005)
• The 2004 MIT Lincoln Laboratory Speaker Recognition System
– Reynolds et al (2005)
• The MIT Lincoln Laboratory 2008 Speaker Recognition System
– Sturim, Campbell, Karam, Reynolds, Richardson (2009)

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Douglas A. Reynolds
• PhD (Georgia Tech, 1992)
• Currently Senior Member of
Technical Staff at MIT Lincoln Lab
• Most cited author in speaker
recognition (by far?)
• Contributed several key ideas currently used in
robust speaker recognition systems
• MIT Lincoln Lab has won numerous awards at
the NIST SRE over the years
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What can we learn from speech?
Slide courtesy: Reynolds, Heck
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Speaker Recognition
Identification Verification
• No identity claim is • Identity claim is made
made
• Binary decision
• Classification
• Open-set vs closed-set
• Text-dependent vs text-independent
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Applications
• (Telephonic) Transaction Authentication
• Access Control
– Physical facilities
– Computer and data networks
• Parole Monitoring
• Information Retrieval
– Audio indexing in call centers
• Forensics

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Components of a speaker recognition
system
Universal
Background Model
Background’s “Voiceprint”
Slide courtesy: Reynolds, Heck
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Phases of speaker verification
Slide courtesy: Reynolds, Heck
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Feature Extraction
Universal
Background Model
Background’s “Voiceprint”
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Feature Extraction
• Pre-processing
– Bandlimiting
– Silence, noise removal
– Channel bias removal (RASTA et al)
• Feature computation
– MFCC computed every 10ms over a 20ms window
– F0 and energy features
– Phonetic features

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