Automatic Speaker Recognition

Qian Yang

04. June, 2013

Outline

Overview

Traditional Approaches

Speaker Diarization

State-of-the-art speaker recognition systems use:

GMM-based framework

SVM-based framework

Outline

Overview

Traditional Approaches

Features and Models

Evaluation and Performance

Speaker Diarization

Primary features used in speaker recognition systems are cepstral features (eg. MFCC, PLP)

Voice activity detection (VAD) to remove non-speech frames

Some form of blind decovolution is used to remove stationary channel effects (CMS)

Feature warping is to compensate channel variability

Time differential cepstral (delta cepstra, or delta detla features) are usually appended to cepstral features

Typically 24-40 dimensional features are used

Traditional Approaches

Features for Speaker Recognition

Support Vector Machines

Latest discriminative approach for speaker verification

Traditonal Approaches Support Vector Machines

A SVM is a supervised method

• A SVM is a binary discriminative classifier ( target speaker vs.

impostors)

• transform data to a higher dimensional space using kernel functions

• Construct a hyperplane in higher dimensional space to maximize the margin between support vectors

• The data points lying on the boundaries are support vectors

• Design different kernels for speaker recognition task kernel

Ideal output (0/1)

Support vectors

When classification, a class decision is based upon whether the value, f(x), is above or below a threshold

• How to represent speaker utterances in higher dimensional space ??

• Example: A linear kernel for speaker verification task

– Train GMMs on two utterances using MAP

– Derive a distance metric between two utterances using a modified KL divergence

– Define a linear kernel based on the distance above

Traditonal Approaches Support Vector Machines

Outline

Overview

Traditional Approaches

Speaker Diarization

Overview

Cross-show speaker diarization

Speaker Tracking

Unlike speaker verification/identifcation, no enrollment data for training and

no prior knowledge about number of speakers !!!

Speaker Diarization Generic Architecture

3-step Diarization process

1. Voice Activity Detection (VAD)

Detects different acoustic events: music, speech, noise ..

2. Speaker Change Detection

Detects speaker turn changes missed by VAD

3. Speaker Clustering

Group speaker segments from same speaker together

Cross-show Speaker Diarization

Giving a set of shows from same source, provides global

IDs for speakers who appear across shows.

Why cross-show?

In digital libraries or multimedia archives, some

speakers may appear multiple times, such as

journalists, politicians ..

Linkage to conventional speaker diarization

Global IDs vs. Local IDs

Cross-show Speaker Diarization

Generic Architectures

Scheme 1

Scheme 2 Scheme 3

Cross-show Speaker Diarization

Some results on English Podcast News data

Cross-show DER as metrics

Speaker Tracking

Aims to determine if and when target speaker speaks in a multi-speaker record. The target speaker has been enrolled into the system in the previous phase.

Why speaker tracking?

Tracking anchor speakers or politicians on broadcast news

Linkage to speaker diarization

Supervised approach (speaker models are required)

Diarization results are used as inputs

KIT’s speaker tracking system

Consists of two components: speaker segementation + Open-set speaker identification

System Architecture

Speaker segmentation

Split the speech into segments. Each segment has only one speaker speaking

Open-set speaker identification(SID)

- Training Phase:

• train 4 UBMs for telephone/studio male/female speech (gender and channel dependent) on ESTER2 data

• Speaker models obtained by MAP adapted on corresponding UBMs

- Detection Phase:

• System 1(baseline) - gender/bandwidth classification (GMM-based classifier), test segment scored against speaker and UBM models which matches gender/bandwidth conditions

• System 2 (FSC) - Apply frame-base score competition (FSC) [JSW07]

- 100 impostors(50F+50M) for T-Norm from ESTER1 data

Some results on French broadcast news

ESTER2 data, French BN

114 target speakers, 105h training data, 6 hours dev data

Metrics: Half total error rate (HTER)

System HTER-time HTER-speaker

Baseline 25.307% -- 31.943% --

Baseline+TNorm 25.314% -0.03% 31.953% -0.03%

FSC 24.098% 4.78% 31.319% 1.95%

FSC+TNorm 27.830% -9.97% 33.260% -4.12%

Thanks for your attention!

Questions?