Learning the parameters of quantitative prosody models

Jokisch, Oliver; Mixdorff, Hansjörg; Kruschke, Hans; Kordon, Ulrich

doi:10.21437/icslp.2000-160

Cited by 13 publications

(1 citation statement)

References 3 publications

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“…The F 0 -model was already introduced in corpus-based F 0 contour synthesis works [5,6], but they require input parameters to the prediction module not easily be obtainable from the text.…”

Section: Introductionmentioning

confidence: 99%

Corpus-based synthesis of fundamental frequency contours based on a generation process model

Hirose¹,

Eto²,

Minematsu³

et al. 2001

7th European Conference on Speech Communication and Technology (Eurospeech 2001)

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A mode-constrained corpus-based synthesis strategy was developed for fundamental frequency (F 0 ) contours of Japanese sentences. In the training phase, the relationship between linguistic factors and the command values (amplitudes and locations) of F 0 contour generation process model was learned for a prediction module; a neural network in the current paper. Input parameters consist of linguistic information related to accentual phrases that can be automatically driven from text, such as the position of the accentual phrase in the utterance, number of morae, accent type, and morphological information. In the synthesis phase, the prediction module is used to generate command values of the model. The synthesis method was also realized based on multiple linear regression analysis to examine how each input parameter contributes to the F 0 contour generation. The use of the parametric model restricts the degrees of freedom of the mapping between linguistic and prosodic features, and thus enables to generate appropriate values even with limited training data. Experimental results showed that the method could generate F 0 contours quite close to those by the rulebased method.

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“…The F 0 -model was already introduced in corpus-based F 0 contour synthesis works [5,6], but they require input parameters to the prediction module not easily be obtainable from the text.…”

Section: Introductionmentioning

confidence: 99%

Corpus-based synthesis of fundamental frequency contours based on a generation process model

Hirose¹,

Eto²,

Minematsu³

et al. 2001

7th European Conference on Speech Communication and Technology (Eurospeech 2001)

View full text Add to dashboard Cite

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Visualization of Prosodic Knowledge Using Corpus Driven MEMOInt Intonation Modelling

Mancebo

Cardeñoso-Payo

2006

Text, Speech and Dialogue

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Abstract. In this work we show how our intonation corpus driven intonation modelling methodology MEMOInt can help in the graphical visualization of the complex relationships between the different prosodic features which configure the intonational aspects of natural speech. MEMOInt has already been used successfully for the prediction of synthetic F0 contours in the presence of the usual data scarcity problems. Now, we report on the possibilities of using the information gathered in the modelling phase in order to provide a graphical view of the relevance of the various prosodic features which affect the typical F0 movements. The set of classes which group the intonation patterns found in the corpus can be structured in a tree in which the relation between the classes and the prosodic features of the input text is hierarchically correlated. This visual outcome shows to be very useful to carry out comparative linguistic studies of prosodic phenomena and to check the correspondence between previous prosodic knowledge on a language and the real utterances found in a given corpus.

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Mining Intonation Corpora Using Knowledge Driven Sequential Clustering

Mancebo

Cardeñoso-Payo

2006

Advances in Artificial Intelligence - IBERAMIA-SBIA 2006

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Abstract. This work presents a mining methodology designed to cope with the usual data scarcity problems of intonation corpora which arises from the high variability of prosodic information. The methodology is an adaptation of a basic agglomerative clustering technique, guided by a set of domain constraints. The peculiarities of the text-to-speech intonation modelling problem are considered in order to fix the initial configuration of the cluster and the criteria to merge classes and stopping their splitting. The scarcity problem poses the need to apply a sequential selection mechanism of prosodic features, in order to obtain the initial set of classes in the cluster. A searching strategy to select the best class among a set of alternatives is proposed, which provides useful prediction models for accurate synthetic intonation. Visualization of final classes by means of a modified decision tree brings graphical cues about contrastable prosodic information of the intonation corpus.

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Learning the parameters of quantitative prosody models

Cited by 13 publications

References 3 publications

Corpus-based synthesis of fundamental frequency contours based on a generation process model

Corpus-based synthesis of fundamental frequency contours based on a generation process model

Visualization of Prosodic Knowledge Using Corpus Driven MEMOInt Intonation Modelling

Mining Intonation Corpora Using Knowledge Driven Sequential Clustering

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