Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Lazaridis,

doi:10.3844/jcssp.2010.341.349

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…The biggest decrease of RMSE in percentages was obtained for the full phoneme set, whereas the percentage of RMSE decrease for consonants is the smallest. 20.30 0.79 Greek [9] 26.40 0.54 Greek [10] 27.20 0.63 Greek vowels [11] 26.04 -Greek consonants [11] 29.13 -Lithuanian vowels [12] 18.30 0.80 Lithuanian consonants [12] 16.70 0.75 Serbo-Croatian [13] 15.85 0.91 Korean [14] 22.00 0.82 Turkish [15] 20.04 0.78 Hindi [16] 27.14 0.75 Telugu [16] 22.86 0.80…”

Section: Resultsmentioning

confidence: 99%

Tree-based Phone Duration Modelling of the Serbian Language

Sovilj-Nikic

Delić

Sovilj-Nikić

et al. 2014

ElAEE

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Considering the importance of segmental duration from a perceptive point of view, the possibility of automatic prediction of natural duration of phones is essential for achieving the naturalness of synthesized speech. In this paper phone duration prediction model for the Serbian language using tree-based machine learning approach is presented. A large speech corpus and a feature set of 21 parameters describing phones and their contexts were used for segmental duration prediction. Phone duration modelling is based on attributes such as the current segment identity, preceding and following segment types, manner of articulation (for consonants) and voicing of neighbouring phones, lexical stress, part-of-speech, word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. These features have been extracted from the large speech database for the Serbian language. The results obtained for the full phoneme set using regression tree, RMSE (root-mean-squared-error) 14.8914 ms, MAE (mean absolute error) 11.1947 ms and correlation coefficient 0.8796 are comparable with those reported in the literature for Czech, Greek, Lithuanian, Korean, Indian languages Hindi and Telugu, Turkish.Index Terms-Decision trees, machine learning algorithms, speech, speech synthesis.

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Section: Resultsmentioning

confidence: 99%

Tree-based Phone Duration Modelling of the Serbian Language

Sovilj-Nikic

Delić

Sovilj-Nikić

et al. 2014

ElAEE

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“…These models require a large corpus of spoken language, because the modeling is done using a machine learning algorithm on large corpora. Various machine learning approaches have been applied for phone duration modeling such as artificial neural networks [3,4] decision trees [5][6][7][8][9][10], Bayesian models [11], and instance-based algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Meta Learning Approach to Phone Duration Modeling

Sovilj-Nikic¹,

Sovilj-Nikić

Marković

2018

Teh. vjesn.

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One of the essential prerequisites for achieving the naturalness of synthesized speech is the possibility of the automatic prediction of phone duration, due to the high importance of segmental duration in speech perception. In this paper we present a new phone duration prediction model for the Serbian language using meta learning approach. Based on the data obtained from the analysis of a large speech database, we used a feature set of 21 parameters describing phones and their contexts. These include attributes related to the segmental identity, manner of articulation (for consonants), attributes related to phonological context, such as segment types and voicing values of neighboring phones, presence or absence of lexical stress, morphological attributes, such as part-of-speech, and prosodic attributes, such as phonological word length, the position of the segment in the syllable, the position of the syllable in a word, the position of a word in a phrase, phrase break level, etc. Phone duration model obtained using meta learning algorithm outperformed the best individual model by approximately 2,0% and 1,7% in terms of the relative reduction of the root-meansquared error and the mean absolute error, respectively.

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The development of phone duration model in speech synthesis in the Serbian language

Sovilj-Nikic

Sovilj-Nikić

2015

2015 23rd Telecommunications Forum Telfor (TELFOR)

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Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Cited by 5 publications

References 21 publications

Tree-based Phone Duration Modelling of the Serbian Language

Tree-based Phone Duration Modelling of the Serbian Language

Meta Learning Approach to Phone Duration Modeling

The development of phone duration model in speech synthesis in the Serbian language

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