An Auditory Scene Analysis Approach to Speech Segregation

DeLiang Wang

Perception and Neurodynamics Lab

The Ohio State University

Outline of presentation

Introduction Speech segregation problem Auditory scene analysis (ASA) approach

Voiced speech segregation based on pitch tracking and amplitude modulation analysis

Ideal binary mask as CASA goal Unvoiced speech segregation

Auditory segmentation Neurobiological basis of ASA

Real-world auditionWhat?• Source type

Speechmessagespeaker

age, gender, linguistic origin, mood, …

MusicCar passing by

Where?• Left, right, up, down• How close?Channel characteristicsEnvironment characteristics• Room configuration• Ambient noise

Humans versus machines

Source: Lippmann (1997)

Additionally:• Car noise is not a very

effective speech masker

• At 10 dB

• At 0 dB

• Human word error rate at 0 dB SNR is around 1% as opposed to 100% for unmodified recognisers (around 40% with noise adaptation)

Speech segregation problem

• In a natural environment, speech is usually corrupted by acoustic interference. Speech segregation is critical for many applications, such as automatic speech recognition and hearing prosthesis

• Most speech separation techniques, e.g. beamforming and blind source separation via independent analysis, require multiple sensors. However, such techniques have clear limits• Suffer from configuration stationarity• Can’t deal with single-microphone mixtures or situations where

multiple sounds arrive from close directions

• Most speech enhancement developed for monaural situation can deal with only stationary acoustic interference

Auditory scene analysis (Bregman’90)

Listeners are able to parse the complex mixture of sounds arriving at the ears in order to retrieve a mental representation of each sound source Ball-room problem, Helmholtz, 1863 (“complicated beyond

conception”) Cocktail-party problem, Cherry’53

Two conceptual processes of auditory scene analysis (ASA): Segmentation. Decompose the acoustic mixture into sensory

elements (segments) Grouping. Combine segments into groups, so that segments in the

same group are likely to have originated from the same environmental source

Computational auditory scene analysis

Computational ASA (CASA) systems approach sound separation based on ASA principles Weintraub’85, Cooke’93, Brown & Cooke’94, Ellis’96, Wang &

Brown’99

CASA progress: Monaural segregation with minimal assumptions

CASA challenges Broadband high-frequency mixtures Reliable pitch tracking of noisy speech Unvoiced speech

Outline of presentation

Introduction Speech segregation problem Auditory scene analysis (ASA) approach

Voiced speech segregation based on pitch tracking and amplitude modulation analysis

Ideal binary mask as CASA goal Unvoiced speech segregation

Auditory segmentation Neurobiological basis of ASA

Resolved and unresolved harmonics

For voiced speech, lower harmonics are resolved while higher harmonics are not

For unresolved harmonics, the envelopes of filter responses fluctuate at the fundamental frequency of speech

Our model (Hu & Wang’04) applies different grouping mechanisms for low-frequency and high-frequency signals: Low-frequency signals are grouped based on periodicity and

temporal continuity High-frequency signals are grouped based on amplitude modulation

(AM) and temporal continuity

Diagram of the Hu-Wang model

Unit Labeling

Mixture

Peripheral and

mid-level processing

Initial Segregation

Resynthesis

Segregated speech

Pitch Tracking

Final Segregation

Cochleogram: Auditory peripheral model

Spectrogram• Plot of log energy across time and

frequency (linear frequency scale)

Cochleogram• Cochlear filtering by the gammatone

filterbank (or other models of cochlear filtering), followed by a stage of nonlinear rectification; the latter corresponds to hair cell transduction by either a hair cell model or simple compression operations (log and cube root)

• Quasi-logarithmic frequency scale, and filter bandwidth is frequency-dependent

• Previous work suggests better resilience to noise than spectrogram

Spectrogram

Cochleogram

Mid-level representations form the basis for segment formation and subsequent grouping

Correlogram extracts periodicity and AM from simulated auditory nerve firing patterns

Summary correlogram is used to identify global pitch Cross-channel correlation between adjacent

correlogram channels identifies regions that are excited by the same harmonic or formant

Mid-level auditory representations

Correlogram

• Short-term autocorrelation of the output of each frequency channel of the cochleogram

• Peaks in summary correlogram indicate pitch periods (F0)

• A standard model of pitch perception

Correlogram & summary correlogram of a double vowel, showing F0s

Cross-channel correlation

(a) Correlogram and cross-channel correlation of hair cell response to clean speech

(b) Corresponding representations for response envelopes

Initial segregation

Segments are formed based on temporal continuity and cross-channel correlation

Segments generated in this stage tend to reflect resolved harmonics, but not unresolved ones

Initial grouping into a foreground (target) stream and a background stream according to global pitch using the oscillatory correlation model of Wang and Brown (1999)

Pitch tracking

Pitch periods of target speech are estimated from the segregated speech stream

Estimated pitch periods are checked and re-estimated using two psychoacoustically motivated constraints: Target pitch should agree with the periodicity of the time-frequency

units in the initial speech stream Pitch periods change smoothly, thus allowing for verification and

interpolation

Pitch tracking example

(a) Global pitch (Line: pitch track of clean speech) for a mixture of target speech and ‘cocktail-party’ intrusion

(b) Estimated target pitch

T-F unit labeling

In the low-frequency range: A time-frequency (T-F) unit is labeled by comparing the periodicity

of its autocorrelation with the estimated target pitch

In the high-frequency range: Due to their wide bandwidths, high-frequency filters respond to

multiple harmonics. These responses are amplitude modulated due to beats and combinational tones (Helmholtz, 1863)

A T-F unit in the high-frequency range is labeled by comparing its AM repetition rate with the estimated target pitch

AM example

(a) The output of a gammatone filter (center frequency: 2.6 kHz) in response to clean speech

(b) The corresponding autocorrelation function

AM repetition rates

To obtain AM repetition rates, a filter response is half-wave rectified and bandpass filtered

The resulting signal within a T-F unit is modeled by a single sinusoid using the gradient descent method. The frequency of the sinusoid indicates the AM repetition rate of the corresponding response

Final segregation

New segments corresponding to unresolved harmonics are formed based on temporal continuity and cross-channel correlation of response envelopes (i.e. common AM). Then they are grouped into the foreground stream according to AM repetition rates

Other units are grouped according to temporal and spectral continuity

Ideal binary mask for performance evaluation

Within a T-F unit, the ideal binary mask is 1 if target energy is stronger than interference energy, and 0 otherwise

Motivation: Auditory masking - stronger signal masks weaker one within a critical band

We have suggested to use ideal binary masks as ground truth for CASA performance evaluation Consistent with recent speech intelligibility results (Roman et al.’03;

Brungart et al.’05)

Ideal binary mask illustration

Voiced speech segregation example

Systematic SNR results

Evaluation on a corpus of 100 mixtures (Cooke, 1993): 10 voiced utterances x 10 noise intrusions (see next slide)

Average SNR gain: 12.3 dB; 5.2 dB better than the Wang-Brown model (1999), and 6.4 dB better than the spectral subtraction method

Hu-Wang model

-7

-2

3

8

13

18

N0 N1 N2 N3 N4 N5 N6 N7 N8 N9

Mixture Hu-Wang model

Spectral Subtraction Wang-Brown model

SN

R (

in d

B)

CASA progress on voiced speech segregation

• 100 mixture set used by Cooke (1993)• 10 voiced utterances mixed with 10 noise intrusions (N0: tone, N1: white

noise, N2: noise bursts, N3: ‘cocktail party’, N4: rock music, N5: siren, N6: telephone, N7: female utterance, N8: male utterance, N9: female utterance)

Cooke (1993)

Ellis (1996)

Wang & Brown(1999)

Hu & Wang(2004)

+ telephone

+ male

+ female

Original mixtureof voiced speech

Outline of presentation

Introduction Speech segregation problem Auditory scene analysis (ASA) approach

Voiced speech segregation based on pitch tracking and amplitude modulation analysis

Ideal binary mask as CASA goal Unvoiced speech segregation

Auditory segmentation Neurobiological basis of ASA

Segmentation and unvoiced speech segretation

• To deal with unvoiced speech segregation, we (Hu & Wang’04) proposed a model of auditory segmentation that applies to both voiced and unvoiced speech

• The task of segmentation is to decompose an auditory scene into contiguous T-F regions, each of which should contain signal from the same sound source• The definition of segmentation does not distinguish between voiced

and unvoiced sounds

• This is equivalent to identifying onsets and offsets of individual T-F regions, which generally correspond to sudden changes of acoustic energy

• The segmentation strategy is based on onset and offset analysis

Scale-space analysis for auditory segmentation

• From a computational standpoint, auditory segmentation is similar to image (visual) segmentation• Visual segmentation: Finding bounding contours of visual objects

• Auditory segmentation: Finding onset and offset fronts of segments

• Onset/offset analysis employs scale-space theory, which is a multiscale analysis commonly used in image segmentation• Smoothing

• Onset/offset detection and onset/offset front matching

• Multiscale integration

Example of auditory segmentationF

requ

ency

(H

z)

Time (s) 0 0.5 1 1.5 2 2.5

50

363

1246

3255

8000

Speech segregation

• The general strategy for speech segregation is to first segregate voiced speech using the pitch cue, and then deal with unvoiced speech

• To segregate unvoiced speech, we perform auditory segmentation, and then group segments that correspond to unvoiced speech

Segment classification

• For nonspeech interference, grouping is in fact a classification task – to classify segments as either speech or non-speech

• The following features are used for classification:• Spectral envelope• Segment duration• Segment intensity

• Training data• Speech: Training part of the TIMIT database• Interference: 90 natural intrusions including street noise, crowd noise,

wind, etc.

• A Gaussian mixture model is trained for each phoneme, and for interference as well which provides the basis for a likelihood ratio test

Example of segregating fricatives/affricates

Utterance: “That noise problem grows more annoying each day”Interference: Crowd noise with music (IBM: Ideal binary mask)

Example of segregating stops

Utterance: “A good morrow to you, my boy”Interference: Rain

Outline of presentation

Introduction Speech segregation problem Auditory scene analysis (ASA) approach

Voiced speech segregation based on pitch tracking and amplitude modulation analysis

Ideal binary mask as CASA goal Unvoiced speech segregation

Auditory segmentation Neurobiological basis of ASA

How does the auditory system perform ASA?

Information about acoustic features (pitch, spectral shape, interaural differences, AM, FM) is extracted in distributed areas of the auditory system

Binding problem: How are these features combined to form a perceptual whole (stream)?

Hierarchies of feature-detecting cells exist, but do not seem to constitute a solution to the binding problem

Oscillatory correlation theory for ASA

Neural oscillators are used to represent auditory features Oscillators representing features of the same source are

synchronized, and are desynchronized from those representing different sources

Originally proposed by von der Malsburg & Schneider (1986), and further developed by Wang (1996)

Supported by growing experimental evidence

Oscillatory correlation representation

FD: Feature

Detector

FD1

FDn

FD2

FDa

FDb

FDx

Time

Time

Oscillatory correlation for ASA

LEGION dynamics (Terman & Wang’95) provides a computational foundation for the oscillatory correlation theory

The utility of oscillatory correlation has been demonstrated for speech segregation (Wang-Brown’99), modeling auditory attention (Wrigley-Brown’04), etc.

Summary

CASA approach to monaural speech segregation Performs substantially better than previous CASA

systems for voiced speech segregation AM cue and target pitch tracking are important for performance

improvement Early steps for unvoiced speech segregation

Auditory segmentation based on onset/offset analysis Segregation using speech classification

Oscillatory correlation theory for ASA

Acknowledgment

Joint work with Guoning Hu Funded by AFOSR/AFRL and NSF