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