Segment duration in spoken korean

Chung, Hyunsong

doi:10.21437/icslp.2002-366

Cited by 13 publications

(2 citation statements)

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“…Commonly used machine learning methods include Classification And Regression Trees (CART) [1], Quantification Method Type 1 [6], Generalized Linear Models (GLM) [14], and Additive-Multiplicative models [3].…”

Section: Introductionmentioning

confidence: 99%

Comparing QMT1 and HMMs for the synthesis of american English prosody

Krstulovic,

Latorre,

Buchholz

2008

Speech Prosody 2008

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Three models are compared for the duration and pitch contour of American English in a speech synthesis framework. These models combine duration prediction by Quantification Method Type 1 (QMT1), a Codebook-based method for the F0 contour and a Hidden Markov Model-based method for both durations and F0. Subjective listening tests show that the HMMs are preferred over the Codebook for the F0 contour, but that their duration modelling performances are not significantly different from those of QMT1 in the tested setup. An analysis of naive freeform listener comments supports this fact, and suggests that such comments can give useful hints regarding the performance of each system.

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Section: Introductionmentioning

confidence: 99%

Comparing QMT1 and HMMs for the synthesis of american English prosody

Krstulovic,

Latorre,

Buchholz

2008

Speech Prosody 2008

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“…Other neural networkbased models were also presented for Spanish [8] and Arabic [9]. Example of a CART-based model applied for Korean can be found in [10].…”

Section: Introductionmentioning

confidence: 99%

Segmental durations predicted with a neural network

Teixeira¹,

Freitas²

2003

8th European Conference on Speech Communication and Technology (Eurospeech 2003)

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This paper presents a segmental durations' model applied to the European Portuguese language for TTS purposes. The model is based on a feed-forward neural network, trained with a back-propagation algorithm, and has as input a set of phonological and contextual features, automatically extracted from the text. The relative importance of each feature, concerning the correlation with segmental durations and improvements in the performance of the model, is presented. Finally the model is evaluated objectively and subjectively by a perceptual test.

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Evaluation of a Segmental Durations Model for TTS

Teixeira

Freitas

2003

Lecture Notes in Computer Science

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Abstract:In this paper we present a condensed description of a European Portuguese segmental duration's model for TTS purposes and concentrate on its evaluation. This model is based on artificial neural networks. The evaluation of the model quality was made by comparison with read speech. The standard deviation reached in test set is 19.5 ms and the linear correlation coefficient is 0.84. The model is perceptually evaluated with 4.12 against 4.30 for natural human read speech in a scale of 5.

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Segment duration in spoken korean

Cited by 13 publications

References 0 publications

Comparing QMT1 and HMMs for the synthesis of american English prosody

Comparing QMT1 and HMMs for the synthesis of american English prosody

Segmental durations predicted with a neural network

Evaluation of a Segmental Durations Model for TTS

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