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Text-Constrained Speaker Recognition Using Hidden Markov Models

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Text-Constrained Speaker Recognition Using Hidden Markov Models. Kofi A. Boakye International Computer Science Institute. Outline. Introduction Design and System Description Initial Results System Enhancements More words Higher order cepstra Cepstral Mean Subtraction Conclusions

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slide1

Text-Constrained Speaker Recognition

Using Hidden Markov Models

Kofi A. Boakye

International Computer Science Institute

slide2

Outline

  • Introduction
  • Design and System Description
  • Initial Results
  • System Enhancements
    • More words
    • Higher order cepstra
    • Cepstral Mean Subtraction
  • Conclusions
  • Future Work
slide3

Introduction

  • Speaker Recognition Problem: Determine if spoken segment is putative target
    • Also referred to as Speaker Verification/Authentication
slide4

Introduction

Method of Solution Requires Two Phases:

Similar to speech recognition, though “noise” (inter-speaker variability) is now signal.

Training Phase

Testing Phase

claimed identity: Sally

slide5

Introduction

  • Also like speech recognition, different domains exist
  • Two major divisions:
  • Text-dependent/Text-constrained
  • Highly constrained text spoken by person
  • Examples: fixed phrase, prompted phrase
  • Text-independent
  • Unconstrained text spoken by person
  • Example: conversational speech
slide6

Introduction

  • Text-dependent systems can have high performance because of input constraints
    • More acoustic variation arises from speaker distinction(vs. phones)
    • Text-independent systems have greater flexibility
slide7

Introduction

Question: Is it possible to capitalize on advantages of text dependent systems in text-independent domains?

Answer: Yes!

slide8

Introduction

Idea: Limit words of interest to a select group-Words should have high frequency in domain-Words should have high speaker-discriminative quality

What kind of words match these criteria for conversational speech ?1) Discourse markers (like, well, now…)2) Filled pauses (um, uh)

3) Backchannels (yeah, right, uhhuh, …)

These words are fairly spontaneous and represent an “involuntary speaking style” (Heck, WS2002)

slide9

Design

Likelihood Ratio Detector:

Λ = p(X|S) /p(X|UBM)

Task is a detection problem, so use likelihood ratio detector

-In implementation, log-likelihood is used

Speaker

Model

> Θ Accept

< Θ Reject

Feature

Extraction

/

Λ

signal

adapt

Background

Model

slide10

Design

  • State-of-the Art Speaker Recognition Systems use Gaussian Mixture Models
  • Speaker’s acoustic space is represented by many-component mixture of Gaussians

speaker 1

speaker 2

slide11

Design

  • Speaker models are obtained via adaptation of a Universal Background Model (UBM)
  • Probabilistically align target training data into UBM mixture states
  • Update mixture weights, means and variances based on the number of occurrences in mixtures
  • Gives very good performance, but…

Target training data

slide12

Design

  • Concern: GMMs utilize a “bag-of-frames” approach
  • Frames assumed to be independent
  • Sequential information is not really utilized
  • Alternative: Use HMMs
  • Do likelihood test on output from recognizer, which is an accumulated log-probability score
  • Text-independent system has been analyzed (Weber et al. from Dragon Systems)
  • Let’s try a text-dependent one!
slide13

System

Word-level HMM-UBM detectors

HMM-UBM

1

Combination

Word

Extractor

HMM-UBM

2

signal

Λ

HMM-UBM

N

Topology:

Left-to-right HMM with self-loops and no skips

4 Gaussian components per state

Number of states related to number of phones and median number of frames for word

slide14

System

HMMs implemented using HMM toolkit (HTK)

-Used for speech recognition

Input features were 12mel-cepstra, first differences, and zeroth order cepstrum (energy parameter)

Adaptation:

Means were adapted using Maximum A Posteriori adaptation

In cases of no adaptation data, UBM was used

-LLR score cancels

slide15

Word Selection

13 Words:

Discourse markers: {actually, anyway, like, see, well, now}

Filled pauses: {um, uh}

Backchannels: {yeah, yep, okay, uhhuh, right }

Words account for approx: 8% of total tokens

slide16

Recognition Task

NIST Extended Data Evaluation:

Training for 1,2,4,8, and 16 complete conversation sides and testing on one side (side duration ~2.5 mins)

Uses Switchboard I corpus

-Conversational telephone speech

Cross-validation method where data is partitioned

Test on one partition; use others for background models and normalization

For project, used splits 4-6 for background and 1 for testing with 8-conversation training

slide17

Scoring

LLR(X) = log(p(X|S)) – log(p(X|UBM))

Target score: output of adapted HMM scoring forced alignment recognition of word from true transcripts (aligned via SRI recognizer)

UBM score: output of non-adapted HMM scoring same forced alignment

Frame normalization:

Word normalization: Average of word-level frame normalizations

N-best normalization: Frame normalization on n best matching (i.e. high log-prob) words

slide18

Initial Results

Observations:

1) Frame norm result = word norm result

2) EER of n-best decreases with increasing n

-Suggests benefit from an increase in data

slide19

Initial Results

Comparable results: Sturim et al. text-dependent GMM

Yielded EER of 1.3%

-Larger word pool (50 words)

-Channel normalization

slide20

Initial Results

Observations:

EERs for most lie in a small range around 7%

-Suggests that words, as a group, share some qualities

-last two may differ greatly partly because of data scarcity

Best word (“yeah”) yielded EER of 4.63% compared with 2.87% for all words

slide22

System Enhancements: New Words

Some discourse markers and backchannels are bigrams

6 Additional Words Bigrams:

Discourse markers:{you_know, you_see, i_think, i_mean}

Backchannels:{i_see, i_know}

Total coverage of ~10% with these additional words

slide23

System Enhancements: New Words

Results

  • EER reduced from 2.87% to 2.53%
  • Significant reduction, especially given the size of coverage increase
slide24

System Enhancements: New Words

Results

  • Observations:
  • Well-performing bigrams have comparable EERs
  • Poorly-performing bigrams suffer from a paucity of data
  • Suggests possibility of frequency threshold for performance
slide25

System Enhancements: More Cepstra

Idea: Higher order cepstra may posses more variability that can be used for speaker discrimination

Input features modified to 19 mel-cepstra from 12

slide26

System Enhancements: More Cepstra

Results

EER Reduced from 2.87% to 1.88%

slide27

System Enhancements: CMS

  • Idea: Channel response may introduce undesirable variability (e.g., the same speaker on different handsets), so try and remove it
  • Common approach is to perform Cepstral Mean Subtraction (CMS)
  • Convolutional effects in the time domain become additive effects in the log power domain:
  • X(,t) = S(,t)C(,t)
  • log|X(,t)|2 = log|S(,t)|2 + log|C(,t)|2
slide28

System Enhancements: CMS

Results

  • EER reduced from 2.87% to 1.35%
  • Poor performance in low false alarm region
  • possibly due to small number of data points
  • also may have removed ‘good’ channel info
slide29

System Enhancements: Combined System

Results

“grab bag” system yields EER of 1.01%

Suffers from same problem of poor performance for low false alarms

slide30

Conclusions

Well performing text-dependent speaker recognition in an unconstrained speech domain is very feasible

Benefit of sequential information appears to have been established

Benefits of higher order cepstra and CMS for input features have been demonstrated

slide31

Future Work

-Analyze performance with ASR output

-Closer analysis of word frequency to performance

-More words!

-Normalizations (Hnorm, Tnorm)

-Examine influence of word context

(e.g., “well” as discourse marker and as adverb)