Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation

Xavi Gonzalvo; Ignasi Iriondo; Joan Claudi Socoró; Francesc Alias; Carlos Monzo

doi:10.1007/978-3-540-77347-4_4

Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation

Xavi Gonzalvo, Ignasi Iriondo, Joan Claudi Socoró, Francesc Alias, Carlos Monzo

Research output: Book chapter › Conference contribution › peer-review

Abstract

Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMMTTS system using an external machine learning technique to help improving the expressiveness. System performance is analysed objectively and subjectively. The experiments were conducted on a reliably labelled speech corpus, whose units were clustered using contextual factors based on the Spanish language. The results show that the CBR-based F0 estimation is capable of improving the HMM-based baseline performance when synthesizing non-declarative short sentences while the durations accuracy is similar with the CBR. or the HMM system.

Original language	English
Title of host publication	Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers
Publisher	Springer Verlag
Pages	78-85
Number of pages	8
ISBN (Print)	3540773460, 9783540773467
DOIs	https://doi.org/10.1007/978-3-540-77347-4_4
Publication status	Published - 2007
Event	International Conference on Nonlinear Speech Processing, NOLISP 2007 - Paris, France Duration: 22 May 2007 → 25 May 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4885 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Nonlinear Speech Processing, NOLISP 2007
Country/Territory	France
City	Paris
Period	22/05/07 → 25/05/07

Access to Document

10.1007/978-3-540-77347-4_4

Cite this

Gonzalvo, X., Iriondo, I., Socoró, J. C., Alias, F., & Monzo, C. (2007). Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation. In Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers (pp. 78-85). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4885 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-77347-4_4

Gonzalvo, Xavi ; Iriondo, Ignasi ; Socoró, Joan Claudi et al. / Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation. Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag, 2007. pp. 78-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation",

abstract = "Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMMTTS system using an external machine learning technique to help improving the expressiveness. System performance is analysed objectively and subjectively. The experiments were conducted on a reliably labelled speech corpus, whose units were clustered using contextual factors based on the Spanish language. The results show that the CBR-based F0 estimation is capable of improving the HMM-based baseline performance when synthesizing non-declarative short sentences while the durations accuracy is similar with the CBR. or the HMM system.",

author = "Xavi Gonzalvo and Ignasi Iriondo and Socor{\'o}, {Joan Claudi} and Francesc Alias and Carlos Monzo",

year = "2007",

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booktitle = "Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers",

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Gonzalvo, X, Iriondo, I , Socoró, JC , Alias, F & Monzo, C 2007, Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation. in Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4885 LNAI, Springer Verlag, pp. 78-85, International Conference on Nonlinear Speech Processing, NOLISP 2007, Paris, France, 22/05/07. https://doi.org/10.1007/978-3-540-77347-4_4

Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation. / Gonzalvo, Xavi; Iriondo, Ignasi ; Socoró, Joan Claudi et al.
Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag, 2007. p. 78-85 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4885 LNAI).

Research output: Book chapter › Conference contribution › peer-review

TY - GEN

T1 - Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation

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N2 - Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMMTTS system using an external machine learning technique to help improving the expressiveness. System performance is analysed objectively and subjectively. The experiments were conducted on a reliably labelled speech corpus, whose units were clustered using contextual factors based on the Spanish language. The results show that the CBR-based F0 estimation is capable of improving the HMM-based baseline performance when synthesizing non-declarative short sentences while the durations accuracy is similar with the CBR. or the HMM system.

AB - Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMMTTS system using an external machine learning technique to help improving the expressiveness. System performance is analysed objectively and subjectively. The experiments were conducted on a reliably labelled speech corpus, whose units were clustered using contextual factors based on the Spanish language. The results show that the CBR-based F0 estimation is capable of improving the HMM-based baseline performance when synthesizing non-declarative short sentences while the durations accuracy is similar with the CBR. or the HMM system.

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Gonzalvo X, Iriondo I , Socoró JC , Alias F, Monzo C. Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation. In Advances in Nonlinear Speech Processing - International Conference on Nonlinear Speech Processing, NOLISP 2007, Revised Selected Papers. Springer Verlag. 2007. p. 78-85. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77347-4_4

Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation

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