Hands-Free Speech Recognition System

Fast-Convergence Blind Source
Separation Combining ICA andBeamforming?

NoiseInterference

December 22, 2004

MicrophoneSpeech recognition system

Tsuyoki NISHIKAWA

Where is the
NARA Station ?

User

Speech recognition performance
significantly degrades.

Speech and Acoustics Laboratory

Blind Source Separation (BSS) Based on
Independent Component Analysis (ICA)

Conventional Approach

BSS is the approach taken to estimate source signals
only from mixed signals.
 ICA (Comon, 1994) using independence between
the source signals is mainly used.
 Goal Realization of a high quality hands-free speech

recognition system

Estimate source signals
 Microphone array Receiver which consists of multiple elements
 To enhance target speech or reduce interference

Speaker1

 Problem of microphone array processing

Goodmorning!

Mutually
Independent A priori information is required.
 Directions of arrival of the sound sources Breaks of target speech for filter adaptation

Source1

Hello!
Speaker2

Frequency-Domain ICA

Source2

Microphone1

Microphone2

Observed signal1

Known

Observed signal2

Problems and Our ApproachConventional ICA-based BSS must
perform a nonlinear optimization.
⇒ Low-convergence problem⇒ Trap on Local-minizer

Nonlinear optimization

Optimize W ( f )so that Y1 ( f , t ) and Y2 ( f , t )are mutually independent.

To combine ICA and beamforming (BF)
Temporal alternation between ICA and BF
through optimization can realize fast- andhigh-convergence.

1

Proposed Algorithm Overview
Combination of ICA and BF
Init W0 ( f )ONE TIME

ICA

Wi + 1 (ICA)( f )

θˆ l

BF

Null Beamforming
to reduce interference
from direct-path
W (BF) ( f )Direction of Arrival (DOA) Estimation(1)To find nulls in the directivity pattern every
frequency bin.
(2) To determine DOAs by the inter-frequencyaveraged null direction.

DOA Estimation

TargetspeechGain

Wi + 1 (ICA)( f )Diversity with Cost Function

W( f )Wi ( f )i = i +1

else

if final

W( f )

Select the most suitable
separation matrix in eachfrequency and each iteration
point.

Null Beamforming(1) Null beamforming is approximately optimal for
signal separation when the effect of thereverberation is negligible(2) However, null beamforming still has theadvantage that there is no difficulty with respect
to the slow convergence of optimization

Gain

Target Interference
speechspeech

Direction
Directivity pattern of separation filter
estimated by ICA

Cost Function for Diversity
Diversity Criterion between ICA and BF⎧W (ICA) ( f ) (J (ICA) ( f ) ≤ J (BF) ( f ) )Wi ( f ) = ⎨ (BF)
(ICA)
( f ) > J (BF) ( f ) )⎩ W ( f ) (JY1 ( f ,t) ⋅Y2 ( f ,t)* t(ICA)

J (ICA)( f ) =Y1 ( f ,t)
(ICA)

(ICA)

2t

12

Experimental ConditionJ (BF)( f ) =⋅ Y2 (ICA)( f ,t)2t

12

Y1 ( f ,t) ⋅Y2 ( f ,t)* t(BF)

Direction
Directivity pattern of separation filter by BF

Interferencespeech

Y1 ( f ,t)(BF)

Goodseparation

(BF)

2t

12

⋅ Y2 (BF)( f ,t)J( f ) → 0
⇓

2t

12

Separation Results
Fast convergence can be achieved

Reverberation time: 300 ms
 Sound source: 12 combinations (2-male and 2-female speech)
 Evaluation: Noise Reduction Rate (Output SNR – Input SNR [dB] )2Demonstration of BSS

Alternation between ICA and BF

Null beamforming is used for
BF
acceleration of learning

Blind separation of two speech signals
 HardwareLaptop PC: Sony VAIO type U

ICA is substituted by BF



ICA is used to obtain separation
matrix accuratelyCPU: Pentium M 1 GHz
Memory: 512 MB

Microphone: Panasonic electret condenser
microphone (Ominidirectional)

Software

Proposed fast-convergence BSSConclusion
NRR improvement by proposed method
 Fast convergence Automatic diversity between ICA and BF


BF is used for acceleration of learning at earlytimes in the iteration because BF is roughapproximation of unmixing matrix.
 ICA is used after the early part because ICA canupdate the unmixing matrix more accurately.
 ICA is substituted by BF through whole iteration
points at low-convergence frequency region.
3