Speaker Adaptation Method for Acoustic-to-Articulatory Inversion using an HMM-Based Speech Production Model

Sadao HIROYA; Masaaki HONDA

Speaker Adaptation Method for Acoustic-to-Articulatory Inversion using an HMM-Based Speech Production Model

Sadao HIROYA, Masaaki HONDA

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We present a speaker adaptation method that makes it possible to determine articulatory parameters from an unknown speaker's speech spectrum using an HMM (Hidden Markov Model)-based speech production model. The model consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping that transforms the articulatory parameters into a speech spectrum for each HMM state. The model is statistically constructed by using actual articulatory-acoustic data. In the adaptation method, geometrical differences in the vocal tract as well as the articulatory behavior in the reference model are statistically adjusted to an unknown speaker. First, the articulatory parameters are estimated from an unknown speaker's speech spectrum using the reference model. Secondly, the articulatory-to-acoustic mapping is adjusted by maximizing the output probability of the acoustic parameters for the estimated articulatory parameters of the unknown speaker. With the adaptation method, the RMS error between the estimated articulatory parameters and the observed ones is 1.65 mm. The improvement rate over the speaker independent model is 56.1 %.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.5 pp.1071-1078

Publication Date: 2004/05/01

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Type of Manuscript: Special Section PAPER (Special Section on Speech Dynamics by Ear, Eye, Mouth and Machine)

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Sadao HIROYA, Masaaki HONDA, "Speaker Adaptation Method for Acoustic-to-Articulatory Inversion using an HMM-Based Speech Production Model" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 5, pp. 1071-1078, May 2004, doi: .
Abstract: We present a speaker adaptation method that makes it possible to determine articulatory parameters from an unknown speaker's speech spectrum using an HMM (Hidden Markov Model)-based speech production model. The model consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping that transforms the articulatory parameters into a speech spectrum for each HMM state. The model is statistically constructed by using actual articulatory-acoustic data. In the adaptation method, geometrical differences in the vocal tract as well as the articulatory behavior in the reference model are statistically adjusted to an unknown speaker. First, the articulatory parameters are estimated from an unknown speaker's speech spectrum using the reference model. Secondly, the articulatory-to-acoustic mapping is adjusted by maximizing the output probability of the acoustic parameters for the estimated articulatory parameters of the unknown speaker. With the adaptation method, the RMS error between the estimated articulatory parameters and the observed ones is 1.65 mm. The improvement rate over the speaker independent model is 56.1 %.
URL: https://global.ieice.org/en_transactions/information/10.1587/e87-d_5_1071/_p

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@ARTICLE{e87-d_5_1071,
author={Sadao HIROYA, Masaaki HONDA, },
journal={IEICE TRANSACTIONS on Information},
title={Speaker Adaptation Method for Acoustic-to-Articulatory Inversion using an HMM-Based Speech Production Model},
year={2004},
volume={E87-D},
number={5},
pages={1071-1078},
abstract={We present a speaker adaptation method that makes it possible to determine articulatory parameters from an unknown speaker's speech spectrum using an HMM (Hidden Markov Model)-based speech production model. The model consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping that transforms the articulatory parameters into a speech spectrum for each HMM state. The model is statistically constructed by using actual articulatory-acoustic data. In the adaptation method, geometrical differences in the vocal tract as well as the articulatory behavior in the reference model are statistically adjusted to an unknown speaker. First, the articulatory parameters are estimated from an unknown speaker's speech spectrum using the reference model. Secondly, the articulatory-to-acoustic mapping is adjusted by maximizing the output probability of the acoustic parameters for the estimated articulatory parameters of the unknown speaker. With the adaptation method, the RMS error between the estimated articulatory parameters and the observed ones is 1.65 mm. The improvement rate over the speaker independent model is 56.1 %.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Speaker Adaptation Method for Acoustic-to-Articulatory Inversion using an HMM-Based Speech Production Model
T2 - IEICE TRANSACTIONS on Information
SP - 1071
EP - 1078
AU - Sadao HIROYA
AU - Masaaki HONDA
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2004
AB - We present a speaker adaptation method that makes it possible to determine articulatory parameters from an unknown speaker's speech spectrum using an HMM (Hidden Markov Model)-based speech production model. The model consists of HMMs of articulatory parameters for each phoneme and an articulatory-to-acoustic mapping that transforms the articulatory parameters into a speech spectrum for each HMM state. The model is statistically constructed by using actual articulatory-acoustic data. In the adaptation method, geometrical differences in the vocal tract as well as the articulatory behavior in the reference model are statistically adjusted to an unknown speaker. First, the articulatory parameters are estimated from an unknown speaker's speech spectrum using the reference model. Secondly, the articulatory-to-acoustic mapping is adjusted by maximizing the output probability of the acoustic parameters for the estimated articulatory parameters of the unknown speaker. With the adaptation method, the RMS error between the estimated articulatory parameters and the observed ones is 1.65 mm. The improvement rate over the speaker independent model is 56.1 %.
ER -