Modeling segmental duration in German text-to-speech synthesis

Möbius, Bernd; Santen, J. Von

doi:10.1109/icslp.1996.607291

Cited by 35 publications

(34 citation statements)

References 16 publications

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“…Database description: During the human speech production procedure the positional and contextual factors of a phone (place in syllable and word) play a very important role in the assessment of its duration (Mobius and Santen, 1996;Santen, 1992). The database was designed following this statement, so as for each phone to have multiple instances in various positions in different words (initial, medial, final) in the database.…”

Section: Meta-learning Algorithmsmentioning

confidence: 99%

“…Various features can be extracted from text for the task of phone duration modeling (Mobius and Santen, 1996;Santen, 1992). The feature set implemented for this task includes phonological, morphological, linguistic and syntactic attributes.…”

Section: Feature Setmentioning

confidence: 99%

“…In human speech communication, prosody refers to the introduction of functions and aspects of speech which may not be encoded by grammar, such as emphasis, intent, attitude or emotional state. In speech, prosody is expressed by factors such as duration (timing and segmental length), fundamental frequency (pitch variations) and energyintensity (loudness) (Klatt, 1987;Mobius and Santen, 1996). For building robust prosody models it is essential to study each of these prosody factors extensively.…”

Section: Introductionmentioning

confidence: 99%

“…The phone duration modeling approaches are divided in two major categories: The rule-based (Klatt, 1979) and the data-driven methods (Mobius and Santen, 1996;Santen, 1992;Chen et al, 1998;Chien and Huang, 2003;Lazaridis et al, 2007). In the rulebased methods manually produced rules, extracted from experimental studies on large sets of utterances or based on previous knowledge, are utilized for determining the duration of segments.…”

Section: Introductionmentioning

confidence: 99%

“…Their main advantage, in contrast to the rule-based techniques, is that this process does not depend on linguists. Over the last years various statistical methods have been applied in the phone duration modeling task such as, Linear Regression (LR) (Takeda et al, 1989), decisions tree-based models (Mobius and Santen, 1996), Sums-Of-Products (SOP) (Santen, 1992). Artificial Neural Networks (ANN) techniques (Chen et al, 1998), Bayesian models (Chien and Huang, 2003) and instance-based algorithms (Lazaridis et al, 2007) have also been introduced on the phone duration modeling task.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Lazaridis¹

2010

Journal of Computer Science

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Problem statement:In this study we cope with the task of phone duration modeling for Greek emotional speech synthesis. Approach: Various well established machine learning techniques are applied for this purpose to an emotional speech database consisting of five archetypal emotions. The constructed phone duration prediction models are built on phonetic, morphosyntactic and prosodic features that can be extracted only from text. We employ model and regression trees, linear regression, lazy learning algorithms and meta-learning algorithms using regression trees as base classifiers, trained on a Modern Greek emotional database consisting of five emotional categories: anger, fear, joy, neutral and sadness. Results: Model trees based on the M5' algorithm and meta-learning algorithms using as base classifier regression trees based on the M5' algorithm proved to perform better. Conclusion: It was observed that the emotional categories of the speech database with the most uniform distribution of phone durations built the most accurate models.

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Section: Meta-learning Algorithmsmentioning

confidence: 99%

Section: Feature Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Lazaridis¹

2010

Journal of Computer Science

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The Bell Labs German text-to-speech system

Möbius

1999

Computer Speech & Language

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Selection of the most significant parameters for duration modelling in a Spanish text-to-speech system using neural networks

Córdoba

Montero

Gutiérrez

et al. 2002

Computer Speech & Language

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Accurate prediction of segmental duration from text in a text-to-speech system is difficult for several reasons. One which is especially relevant is the great quantity of contextual factors that affect timing and it is difficult to find the right way to model them. There are many parameters that affect duration, but not all of them are always relevant and some can even be counterproductive because of the possibility of overtraining.The main motivation of this paper has been to reduce the error in the duration estimation. To this end, it is of the utmost importance to find the factors that most influence duration in a given language. The approach we have taken is to use a neural network, which is completely configurable, and experiment with the different combinations of parameters that yield the minimum error in the estimation.We have oriented our work mainly towards the following aspects: the most significant parameters that can be used as input to the automatic model, and the best way to code these parameters. We have studied first the effect of each parameter alone and, after that, we have included all parameters together to have our final system.Another important aspect of this study is the generation of a suite of software tools and design protocols that will be used in future tasks with different speakers and databases. The applications for automatic modelling are obvious: adapt the prosody to a new speaker, to a new environment, to "restricted-domain" sentences, etc., in a fast, semi-automatic and inexpensive way. After the database labelling, it is a matter of minutes to prepare the inputs to the network for the new situation, and the network is trained in 1 h.The result has been a system that predicts duration with very good results (19 ms in RMS) and that clearly improves our previous rule-based system.

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Modeling segmental duration in German text-to-speech synthesis

Cited by 35 publications

References 16 publications

Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

Comparative Evaluation of Phone Duration Models for Greek Emotional Speech

The Bell Labs German text-to-speech system

Selection of the most significant parameters for duration modelling in a Spanish text-to-speech system using neural networks

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