The Use of Lexica in Text-to-Speech Systems

Quazza, Silvia; Heuvel, H. van den

doi:10.1007/978-94-010-9458-0_7

Cited by 3 publications

(4 citation statements)

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“…The Dutch phonetic transcriptions were automatically generated using the transcription module of the Text-to-Speech (TTS) system developed at the University of Nijmegen [4]. In this transcription module, phone transcriptions of words were obtained by looking up the transcriptions in two lexica: CELEX [1] and ONOMASTICA [9], a lexicon with proper names. For those words for which no transcription was available a grapheme-to-phoneme converter was used.…”

Section: Lexicamentioning

confidence: 99%

Goal-directed ASR in a multimedia indexing and searching environment (MUMIS)

Wester¹,

Kessens²,

Strik³

2002

7th International Conference on Spoken Language Processing (ICSLP 2002)

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This paper describes the contribution of automatic speech recognition (ASR) within the framework of MUMIS (Multimedia Indexing and Searching Environment). The domain is football commentaries. The initial results of carrying out ASR on Dutch and English football commentaries are presented. We found that overall word error rates are high, but application specific words are recognized reasonably well. The difficulty of the ASR task is greatly increased by the high levels of noise present in the material.

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Section: Lexicamentioning

confidence: 99%

Goal-directed ASR in a multimedia indexing and searching environment (MUMIS)

Wester¹,

Kessens²,

Strik³

2002

7th International Conference on Spoken Language Processing (ICSLP 2002)

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“…Eloquens (Quazza & Heuvel, 1997) is a system for Italian using decision trees. The parameters considered in this system are: phoneme identity, stress, window of phonemes, and characteristics of higher order units (syllable, word, and phrase).…”

Section: Introductionmentioning

confidence: 99%

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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“…We have a floating-point value as input. 4) Thermometer: divide all the possible values in different classes (intervals). We activate all the neurons until we get to the current class and leave the remaining neurons inactive.…”

Section: Codification Of the Parametersmentioning

confidence: 99%

“…Many studies have been successfully carried out lately in the field of automatic estimation of a duration model, using different techniques and input parameters to obtain the model. For example, Eloquens [4] is a system for Italian using decision trees. The parameters considered in this system are: phoneme identity, stress, window of phonemes, and characteristics of higher order units (syllable, word, and phrase).…”

Section: Introductionmentioning

confidence: 99%

Duration modeling in a restricted-domain female-voice synthesis in Spanish using neural networks

Córdoba

Montero²,

Gutiérrez-Arriola³

et al.

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)

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The objective of this paper is the accurate prediction of segmental duration in a Spanish text-to-speech system. There are many parameters that affect duration, but not all of them are always relevant. We present a complete environment in which to decide which parameters are more relcvant and the best way to code them. This work is the continuation of [I], where all efforts were dedicated to an unrestricted-domain database for a male voice. In this case, we are considering a female voice in a restricted-domain environment. This restricted-domain offers several advantages to the modeling: the variation in the different pattems is reduced, and so most of the decisions we have made about the parameters arc now based in more significant results. So, the conclusions that we present now show clearly which parameters are best. The system is based in a neural network absolutely configurable.

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The Use of Lexica in Text-to-Speech Systems

Cited by 3 publications

References 1 publication

Goal-directed ASR in a multimedia indexing and searching environment (MUMIS)

Goal-directed ASR in a multimedia indexing and searching environment (MUMIS)

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

Duration modeling in a restricted-domain female-voice synthesis in Spanish using neural networks

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