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Formant-tracking linear prediction model using HMMs and Kalman filters for noisy speech processing

Formant-tracking linear prediction model using HMMs and Kalman filters for noisy speech processing
Formant-tracking linear prediction model using HMMs and Kalman filters for noisy speech processing
This paper presents a formant tracking linear prediction (LP) model for speech processing in noise. The main focus of this work is on the utilization of the correlation of the energy contours of speech, along the formant tracks, for improved formant and LP model estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of the inter-frame correlation of speech parameters across successive speech frames; the within frame correlations are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning spectral amplitude estimation stage where an initial estimate of the LP model of speech for each frame is obtained, (2) a formant classification and estimation stage using probability models of formants and Viterbi-decoders and (3) an inter-frame formant de-noising and smoothing stage where Kalman filters are used to model the formant trajectories and reduce the effect of residue noise on formants. The adverse effects of car and train noise on estimates of formant tracks and LP models are investigated. The evaluation results for the estimation of the formant tracking LP model demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and Kalman smoothing stages, results in a significant reduction in errors and distortions.
0885-2308
543-561
Yan, Qin
f17654ac-1ad3-4e9c-95d8-6014fd5677b2
Vaseghi, Saeed
127f1c21-7861-407e-8552-0a1b9cccf929
Zavarehei, Esfandiar
bdb726fc-0cdf-4db0-9643-7749ee47bcaa
Milner, Ben
5bbb5f4f-ef46-44ec-ac52-40d70cd5941d
Darch, Jonathan
1293b3ac-756e-4b84-8a00-7b7de7a19be3
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Andrianakis, Ioannis
130365dc-7914-4b33-87b2-92eca9da10a5
Yan, Qin
f17654ac-1ad3-4e9c-95d8-6014fd5677b2
Vaseghi, Saeed
127f1c21-7861-407e-8552-0a1b9cccf929
Zavarehei, Esfandiar
bdb726fc-0cdf-4db0-9643-7749ee47bcaa
Milner, Ben
5bbb5f4f-ef46-44ec-ac52-40d70cd5941d
Darch, Jonathan
1293b3ac-756e-4b84-8a00-7b7de7a19be3
White, Paul
2dd2477b-5aa9-42e2-9d19-0806d994eaba
Andrianakis, Ioannis
130365dc-7914-4b33-87b2-92eca9da10a5

Yan, Qin, Vaseghi, Saeed, Zavarehei, Esfandiar, Milner, Ben, Darch, Jonathan, White, Paul and Andrianakis, Ioannis (2007) Formant-tracking linear prediction model using HMMs and Kalman filters for noisy speech processing. Computer Speech & Language, 21 (3), 543-561. (doi:10.1016/j.csl.2006.11.001).

Record type: Article

Abstract

This paper presents a formant tracking linear prediction (LP) model for speech processing in noise. The main focus of this work is on the utilization of the correlation of the energy contours of speech, along the formant tracks, for improved formant and LP model estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of the inter-frame correlation of speech parameters across successive speech frames; the within frame correlations are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning spectral amplitude estimation stage where an initial estimate of the LP model of speech for each frame is obtained, (2) a formant classification and estimation stage using probability models of formants and Viterbi-decoders and (3) an inter-frame formant de-noising and smoothing stage where Kalman filters are used to model the formant trajectories and reduce the effect of residue noise on formants. The adverse effects of car and train noise on estimates of formant tracks and LP models are investigated. The evaluation results for the estimation of the formant tracking LP model demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and Kalman smoothing stages, results in a significant reduction in errors and distortions.

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Published date: July 2007

Identifiers

Local EPrints ID: 46541
URI: http://eprints.soton.ac.uk/id/eprint/46541
ISSN: 0885-2308
PURE UUID: f930bd82-d4ef-45d0-94cb-58871675439e
ORCID for Paul White: ORCID iD orcid.org/0000-0002-4787-8713

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Date deposited: 05 Jul 2007
Last modified: 16 Mar 2024 02:39

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Contributors

Author: Qin Yan
Author: Saeed Vaseghi
Author: Esfandiar Zavarehei
Author: Ben Milner
Author: Jonathan Darch
Author: Paul White ORCID iD
Author: Ioannis Andrianakis

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