Biologically Inspired Noise-Robust Speech Recognition for Both Man and Machine

Mark D. Skowronski

Ph.D. Proposal

University of Florida

Gainesville, FL, USA

Outline

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

Biological Inspiration

Wall Street Journal/Broadcast news readings

Untrained human listeners vs Cambridge HTK LVCSR system

Sound (OLE2)

AWGN:

10 dB SNR

Example of Read Speech:

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

Speech Enhancement

Motivations:

•Noisy cell phone conversations

•Power-constrained transducers

•Public address systems in noisy environments

What can you do when turning up the volume is not an option?

Biology:

Lombard Effect

The Lombard Effect

• Amplitude increases.

• Duration increases.

• Pitch increases.

• Formant frequencies increase.

• High-freq to low-freq energy ratio increases.

• Intelligibility increases.

Lombard Effect: changes in vocal characteristics, produced by a speaker in the presence of background noise.

Psychoacoustic Experiments

• Fletcher (1953): LPF or HPF phonemes varied in robustness to the filtering process, with vowels being the most robust.

• Miller and Nicely (1955): AWGN to speech affects place of articulation and frication most, less so for voicing and nasality.

• Furui (1986): Truncated vowels in consonant-vowel pairs dramatically decreased in intelligibility beyond a certain point of truncation. These points correspond to spectrally dynamic regions.

Speech contains regions of relatively high information content, and emphasis of these regions increases perceived intelligibility.

Solution: Energy RedistributionWe redistribute energy from regions of low information content to regions of high information content while conserving overall energy across words.

N

kj

NN

kj

j

kXN

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SFM

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We partition speech into Voiced/Unvoiced regions using the Spectral Flatness Measure (SFM):

SFM of “clarification”

Xj(k) is the magnitude of the short-term Fourier transform of the jth speech window of length N.

Listening Test

I f, s, x, yes

II a, h, k, 8

III b, c, d, e, g, p, t, v, z, 3

IV m, n

Confusable set test, from Junqua

• 500 trials forced decision

• 3 algorithms (control, ERVU, HPF)

• 0 dB and -10 dB SNR, AWGN

• unlimited playback over headphones

• 26 participants, 30-45 minutes

Listening Test Results-10 dB SNR, white noise

Errors decreased 20% compared to control.

“M”“E”“A”“S”

Energy Redistribution Summary

• Biologically inspired– Lombard Effect says how to modify.

– Psychoacoustic experiments say where to modify.

• Increases intelligibility while maintaining naturalness and conserving energy.

• Naturalness elegantly preserved by retaining spectral and temporal cues.

• Effective because everyday speech is not clearly enunciated.

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

ASR Introduction

• Isolated/Continuous speech

• Dependent/Independent speaker operation

• Word/Phoneme recognition unit

• Vocabulary size and perplexity

Automatic Speech Recognition is the extraction of linguistic

information from an utterance of speech (Text-to-Speech).

Input Feature Extraction Classification

Input

Information: phonetic, gender, age, emotion, pitch, accent, physical state, additive/channel noise

“seven”

Feature Extraction

• Acoustic: formant frequencies, bandwidths• Model based: linear prediction• Filter-bank based: mel freq cepstral coeff (mfcc)

Goal: emphasize phonetic information over other characteristics.

Provides dimensionality reduction on quasi-stationary windows.

Time

Features

“seven”

Hidden Markov Model

“one” Time domain

State space

Feature space

MFCC Algorithm

“seven”

Cepstral domain

DCT

Log energy

Mel-scaled filter bank

F

x(t)

Time

Filter #

MFCC--the most widely-used speech feature extractor.

DCT vs Eigenvectors

Frequency

Spectra of DCT basis vectorsSpectra of Eigenvectors from log energy of filtered speech

Average spectral difference < 15%

Basis #

MFCC Filter Bank

• Design parameters: FB freq range, number of filters.

• Center freqs equally-spaced in mel frequency.

• Triangle endpoints set by center freqs of adjacent filters.

Although filter spacing is determined by perceptual mel frequency scale, bandwidth is set more for convenience than by biological motivation.

Human Factor Cepstral Coefficients

• Decouple filter bandwidth from filter bank design parameters.

• Set filter width according to the critical bandwidth of the human auditory system.

• Use Moore and Glasberg approximation of critical bandwidth, defined in Equivalent Rectangular Bandwidth (ERB).

(Hz) 28.52 93.39 6.23 ERB 2 cc ff

fc is critical band center frequency (KHz).

ASR Experiments Review

• Isolated English digits “zero” through “nine” from TI-46 corpus, 8 male speakers,

• HMM word models, 8 states per model, diagonal covariance matrix,

• Three mfcc versions (different filter banks),• Several degrees of freedom,• Linear ERB scale factor.

ASR Results

White noise (local SNR), hfcc vs D&M

ASR Results

White noise (global SNR), hfcc vs D&M, Linear ERB scale factor (E-factor).

HFCC Conclusions

• Added biologically inspired bandwidth to filter bank of popular speech feature extractor.

• Decoupled bandwidth from other filter bank design parameters.

• Demonstrated superior noise-robust performance of new feature extractor.

• Demonstrated advantages of wider filters.

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

HMM Limitations

• HMMs are piecewise-stationary, while speech is continuous and nonstationary.

• Assumes frames of speech are i.i.d.

• State pdf estimates are data-driven.

HMMs make no claim of modeling biology.

Novel Classifiers• Deng's trended HMM.• Rabiner's autoregression HMM.• Morgan's HMM/neural network hybrid.• Robinson's recurrent neural network.• Wismüller's self-organizing map.• Herrmann's transient attractor network.• Maass' dynamic synapse MLP.• Berger's dynamic synapse RNN.

Freeman's Chaotic Model

• Biologically inspired nonlinear dynamic model of cortical signal processing, from rabbit olfactory neo-cortex experiments.

• A hierarchical network of oscillators that are locally stable and globally chaotic.

• Demonstrated as classifier of static patterns.• Represents a radical departure from current

classifier paradigms.

KI Model

• Smallest element in network hierarchy.

Ni

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• a,b constants

• state variable xi(t)

• N states

• Wij weight from state i to state j

• asymmetric sigmoid Q

• input Ii(t) to state i.

Reduced KII Network

• Locally stable element is KII network.

• m(t) excitatory mitral cell

• g(t) inhibitory granule cell

• Weights Kmg > 0, Kgm < 0

• N pairs in parallel

• Mitral cells fully connected

• Granule cells fully connected

• Input I(t) into excitatory cell.

KII Simulations

m(t)

g(t)

Reduced KII reaches steady state point attractor or limit cycle, based on |Kmg · Kgm|.

• Introduction

• Biologically inspired algorithms– Speech: Energy Redistribution– Features: Human Factor Cepstral Coefficients– Classifier: Nonlinear dynamic systems

• Future work

Work Completed1. Developed biologically inspired algorithms:

• Energy redistribution: combines Lombard Effect (how) with psychoacoustic experimental results (where) to increase speech intelligibility.

• Human factor cepstral coefficients: combines existing speech front end (mfcc) with critical bandwidth information (ERB).

2. Published 3 papers, and submitted 3 more, on novel algorithms.

3. Literature survey on novel speech classifiers, and simulations of nonlinear Freeman model.

Work Proposed

1. Compare hfcc to human speech recognition using rhyming test in ASR experiments.

2. Measure affects of ERVU in ASR experiments.

3. Analyze hfcc algorithm, accounting for nonlinear log(·) function.

4. Experiment with other bandwidth functions besides ERB or scaled ERB.

5. Quantify tradeoff between spectral resolution and noise smoothing for hfcc using synthetic data.

Work Proposed, Con't

6. Build on the reduced KII network results recently reported by CNEL suggesting the network can operate as a content-addressable memory (CAM).

7. Investigate alternative information storage strategies to CAM, focusing on inherent time-varying nature of dynamic system (coupling theory is intriguing).

8. Expand literature search to areas outside speech recognition experiments that use nonlinear dynamic (chaotic) systems for information processing/storage, with emphasis on applications with time-varying signals.

Work Proposed, Con't

9. Consider alternative roles for nonlinear dynamics: embedded extracted features for hfcc/HMM system, trajectory tracking in the spirit of Deng’s trended HMM.

10. Demonstrate classification of static vowel patterns (vowel phonemes) with novel classifier, in presence of noise.

11. Demonstrate classification of time-varying signals (isolated English digits, rhyming test corpus), in noisy environments.