IIT Bombay lehana@iitb.ac.in ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04 Introduction Analysis /...

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1• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04

USE OF HARMONIC PLUS NOISE MODELFOR REDUCTION OF SELF LEAKAGE IN

ELECTROALARYNGEAL SPEECH

Parveen K. Lehana1, Prem C. Pandey2,Santosh S. Pratapwar2, Rockey Gupta1

1University of Jammu, India2IIT Bombay, India

<lehana@iitb.ac.in>

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2• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

ABSTRACTArtificial larynx is an assistive device for providing excitation to vocal tract as a substitute to a dysfunctional or removed larynx. The speech generated by electrolarynx, an external vibrator held against the neck tissue, is not natural and most of the time is unintelligible because of the improper shape of the excitation pulses and presence of a background noise caused by sound leakage from the vibrator. The objective of this paper is to enhance the intelligibility of electrolaryngeal speech by reducing the background noise using harmonic plus noise model (HNM). The alaryngeal speech and the leakage signal are analyzed using HNM and average harmonic spectrum of the leakage noise is subtracted from the harmonic magnitude spectrum of the noisy speech in each frame. HNM synthesis is carried out retaining the original phase spectra. Investigations show that the output is more natural and intelligible as compared to input speech signal and the enhanced signal obtained from spectral subtraction without HNM analysis and synthesis.

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3• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

PRESENTATION OVERVIEW

Introduction HNM Analysis / synthesis Spectral subtraction with HNM Methodology Results Conclusion & future plan

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4• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

INTRODUCTION (1/5)

NATURAL SPEECH PRODUCTION Glottal excitation to vocal tract

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5• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

INTRODUCTION (2/5 )

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6• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

INTRODUCTION (3/5 )

External electronic larynx (transcervical electrolarynx)

Excitation to vocal tract from external vibrator (creates background noise)

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7• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

INTRODUCTION (4/5 )

External electronic larynx (transcervical electrolarynx)

Leakage path:- back side of membrane/plate- improper tissue coupling

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8• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

INTRODUCTION (5/5 )

RESEARCH OBJECTIVEThe objective of this paper is to enhance the intelligibility of electrolaryngeal speech by reducing the background noise using harmonic plus noise model (HNM).

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9• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

HNM ANALYSIS / SYNTHESIS (1/3)

HARMONIC PLUS NOISE MODEL(Stylianou, 1995; 2001)

Speech signal divided into: • harmonic part • noise part

Harmonic part

Noise part

Parameters: • Max. voiced frequency• V/UV & pitch• Harm. ampl. & phases• Noise parameters

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10• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

ANALYSIS / SYNTHESIS WITH HNM (2/3)

ANALYSIS

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11• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

ANALYSIS / SYNTHESIS WITH HNM (3/3)

SYNTHESIS

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12• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

SPECTRAL SUBTRACTION WITH HNM

x(n) = e(n)*hv(n) + e(n)*hl(n) Taking DFT: Xn(ej) = En(ej) [Hvn(ej) + Hln(ej) ]

Assumption:hv(n) & h(n) uncorrelated Xn(ej) 2 = En(ej) 2[Hvn(ej) 2 + Hln(ej) 2]

During non-speech segment: s(n) = 0Xn(ej) 2 = Ln(ej) 2 = En(ej) 2 Hln(ej) 2L(ej) 2 : averaged over many segments

Yn(k) = Xn(k) – L(k) Yn(k) = Yn(k) if Yn(k) L(k)

L(k) otherwise

(: subtraction, : spectral floor, : exp. factors)

Here n is frame index and k is harmonic index

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13• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

METHODOLOGY

STEPS FOR HNM BASED SPECTRAL SUBTRACTION

• Non speech segments analyzed

• Average harmonic spectrum obtained

• Noisy speech analyzed and average harmonic spectrum of noise

subtracted • Resynthesis with noisy speech phase

spectra

For comparison, spectral subtraction using DFT derived magnitude is also carried out.

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14• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

RESULTS (1/2)

Both DFT derived and HNM based harmonic

spectrum significantly reduce the background noiseBoth require empirical selection of the parameters DFT derived spectral subtraction more effective

during non-speechHNM based spectral subtraction more effective

during speech with less musical noise and enhanced

formant structureSaving in parameters and processing time in HNM

based spectral subtraction

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15• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

a) Recorded speech signal

b) Processed (DFT derived) ( = 2, = 0.001, and =1)

c) Processed (HNM derived) ( = 1, = 0.1, and = 1)

RESULTS (2/2)

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16• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan

CONCLUSION HNM based method provides an effective subtraction of noise during the speech and hence can be used for improving intelligibility of electrolaryngeal speech.

FURTHER PLAN

QBNE combined with HNM based spectral subtraction Phase resynthesis from enhanced magnitude spectrumEffect of artificial jitter in pitch on speech quality

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17• Introduction •Analysis / synthesis •Spec. Sub. •Methodology •Results • Conclusion and Future plan