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ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04 USE OF HARMONIC PLUS NOISE MODEL

ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04 USE OF HARMONIC PLUS NOISE MODEL FOR REDUCTION OF SELF LEAKAGE IN ELECTROALARYNGEAL SPEECH Parveen K. Lehana 1 , Prem C. Pandey 2 , Santosh S. Pratapwar 2 , Rockey Gupta 1 1 University of Jammu, India 2 IIT Bombay, India <lehana@iitb.ac.in>.

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ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04 USE OF HARMONIC PLUS NOISE MODEL

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  1. ICSCI 2004, Hyderabad, India, 12-15 Feb’ 04 USE OF HARMONIC PLUS NOISE MODEL FOR REDUCTION OF SELF LEAKAGE IN ELECTROALARYNGEAL SPEECH Parveen K. Lehana1, Prem C. Pandey2, Santosh S. Pratapwar2, Rockey Gupta1 1University of Jammu, India 2IIT Bombay, India <lehana@iitb.ac.in>

  2. ABSTRACT Artificial 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.

  3. PRESENTATION OVERVIEW • Introduction • HNM Analysis / synthesis • Spectral subtraction with HNM • Methodology • Results • Conclusion & future plan

  4. INTRODUCTION (1/5) NATURAL SPEECH PRODUCTION Glottal excitation to vocal tract

  5. INTRODUCTION (2/5 ) If excitation and vocal tract transfer functions are & then output speech is and can be simplified to where

  6. INTRODUCTION (3/5 ) External electronic larynx (transcervical electrolarynx) Excitation to vocal tract from external vibrator (creates background noise)

  7. INTRODUCTION (4/5 ) External electronic larynx (transcervical electrolarynx) Leakage path: - back side of membrane/plate - improper tissue coupling

  8. INTRODUCTION (5/5 ) RESEARCH OBJECTIVE The objective of this paper is to enhance the intelligibility of electrolaryngeal speech by reducing the background noise using harmonic plus noise model (HNM).

  9. 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

  10. ANALYSIS / SYNTHESIS WITH HNM (2/3) ANALYSIS

  11. ANALYSIS / SYNTHESIS WITH HNM (3/3) SYNTHESIS

  12. 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) = 0 Xn(ej) 2 = Ln(ej) 2 = En(ej) 2 Hln(ej) 2 L(ej) 2 : averaged over many segments Yn(k)  = Xn(k)  – L(k)  Yn(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

  13. 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.

  14. RESULTS(1/2) • Both DFT derived and HNM based harmonic spectrum significantly reduce the background noise • Both require empirical selection of the parameters • DFT derived spectral subtraction more effective during non-speech • HNM based spectral subtraction more effective during speech with less musical noise and enhanced formant structure • Saving in parameters and processing time in HNM based spectral subtraction

  15. RESULTS(2/2) a) Recorded speech signal b) Processed (DFT derived) ( = 2,  = 0.001, and  =1) c) Processed (HNM derived) ( = 1,  = 0.1, and  = 1)

  16. 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 spectrum • Effect of artificial jitter in pitch on speech quality

  17. THANKS

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