@article{(Open Science Index):https://publications.waset.org/pdf/5041,
	  title     = {Formant Tracking Linear Prediction Model using HMMs for Noisy Speech Processing},
	  author    = {Zaineb Ben Messaoud and  Dorra Gargouri and  Saida Zribi and  Ahmed Ben Hamida},
	  country	= {},
	  institution	= {},
	  abstract     = {This paper presents a formant-tracking linear prediction
(FTLP) model for speech processing in noise. The main focus of this
work is the detection of formant trajectory based on Hidden Markov
Models (HMM), for improved formant estimation in noise. The
approach proposed in this paper provides a systematic framework for
modelling and utilization of a time- sequence of peaks which satisfies
continuity constraints on parameter; the within peaks are modelled
by the LP parameters. The formant tracking LP model estimation
is composed of three stages: (1) a pre-cleaning multi-band spectral
subtraction stage to reduce the effect of residue noise on formants
(2) estimation stage where an initial estimate of the LP model of
speech for each frame is obtained (3) a formant classification using
probability models of formants and Viterbi-decoders. The evaluation
results for the estimation of the formant tracking LP model tested
in Gaussian white noise background, demonstrate that the proposed
combination of the initial noise reduction stage with formant tracking
and LPC variable order analysis, results in a significant reduction in
errors and distortions. The performance was evaluated with noisy
natual vowels extracted from international french and English vocabulary
speech signals at SNR value of 10dB. In each case, the
estimated formants are compared to reference formants.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {3},
	  number    = {11},
	  year      = {2009},
	  pages     = {2102 - 2107},
	  ee        = {https://publications.waset.org/pdf/5041},
	  url   	= {https://publications.waset.org/vol/35},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 35, 2009},
	}