Croatian HMM-based Speech Synthesis

The paper describes the development of a trainable speech synthesis system, based on hidden Markov models. An approach to speech signal generation using a source-filter model is presented. Inputs into the synthesis system are speech utterances and their phone level transcriptions. A method using context-dependent acoustic models and Croatian phonetic rules for speech synthesis is proposed. Croatian HMM-based speech synthesis experiments are presented and generated speech results are discussed

Automatsko raspoznavanje hrvatskoga govora velikoga vokabulara

This paper presents procedures used for development of a Croatian large vocabulary automatic speech recognition system (LVASR). The proposed acoustic model is based on context-dependent triphone hidden Markov models and Croatian phonetic rules. Different acoustic and language models, developed using a large collection of Croatian speech, are discussed and compared. The paper proposes the best feature vectors and acoustic modeling procedures using which lowest word error rates for Croatian speech are achieved. In addition, Croatian language modeling procedures are evaluated and adopted for speaker independent spontaneous speech recognition. Presented experiments and results show that the proposed approach for automatic speech recognition using context-dependent acoustic modeling based on Croatian phonetic rules and a parameter tying procedure can be used for efficient Croatian large vocabulary speech recognition with word error rates below 5%.Članak prikazuje postupke akustičkog i jezičnog modeliranja sustava za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara. Predloženi akustički modeli su zasnovani na kontekstno-ovisnim skrivenim Markovljevim modelima trifona i hrvatskim fonetskim pravilima. Na hrvatskome govoru prikupljenom u korpusu su ocjenjeni i uspoređeni različiti akustički i jezični modeli. U članku su uspoređ eni i predloženi postupci za izračun vektora značajki za akustičko modeliranje kao i sam pristup akustičkome modeliranju hrvatskoga govora s kojim je postignuta najmanja mjera pogrešno raspoznatih riječi. Predstavljeni su rezultati raspoznavanja spontanog hrvatskog govora neovisni o govorniku. Postignuti rezultati eksperimenata s mjerom pogreške ispod 5% ukazuju na primjerenost predloženih postupaka za automatsko raspoznavanje hrvatskoga govora velikoga vokabulara pomoću vezanih kontekstnoovisnih akustičkih modela na osnovu hrvatskih fonetskih pravila

An Efficient Unit-Selection Method for Concatenative Text-to-Speech Synthesis Systems

This paper presents a method for selecting speech units for polyphone concatenative speech synthesis, in which the simplification of procedures for search paths in a graph accelerated the speed of the unit-selection procedure with minimum effects on the speech quality. The speech units selected are still optimal; only the costs of merging the units on which the selection is based are less accurately determined. Due to its low processing power and memory footprint requirements, the method is suitable for use in embedded speech synthesizers

TEXT-TO-SPEECH SYNTHESIS: A PROTOTYPE SYSTEM FOR CROATIAN LANGUAGE

U radu je prikazan sustav koji omogućuje umjetnu tvorbu hrvatskoga govora prema proizvoljnom ulaznom tekstu. Ulazni tekst, koji mora biti u normaliziranom obliku, sustav pretvara u niz fonema (pretvorba grafem-fonem), a zatim stvara zvučni zapis na temelju fonetskoga niza. Korišteni postupak sinteze temelji se na ulančavanju manjih akustičkih jedinica govora – difona metodom TD-PSOLA. Za potrebe sustava izrađena je i baza difona za hrvatski govor. Predložen je automatski postupak odabira difona iz govornoga korpusa. Kvaliteta ostvarenoga postupka ispitana je provođenjem ankete među ispitanicima. Ispitanici su dali subjektivnu ocjenu kvalitete dobivenoga govora, a time je provjerena i njegova razumljivost.This paper presents the development of a Croatian text-to-speech system capable of synthesizing speech from arbitrary text. Input text in normalized form is first transcribed into a phonetic string (grapheme-to-phoneme conversion) and then processed by a TD-PSOLA based synthesizer. A procedure for automatic selection of diphones from a spoken corpus is proposed. A Croatian language diphone database was built for the system. Subjective quality evaluations of the resulting speech were performed, as well as tests for intelligibility

TEXT-TO-SPEECH SYNTHESIS: A PROTOTYPE SYSTEM FOR CROATIAN LANGUAGE

U radu je prikazan sustav koji omogućuje umjetnu tvorbu hrvatskoga govora prema proizvoljnom ulaznom tekstu. Ulazni tekst, koji mora biti u normaliziranom obliku, sustav pretvara u niz fonema (pretvorba grafem-fonem), a zatim stvara zvučni zapis na temelju fonetskoga niza. Korišteni postupak sinteze temelji se na ulančavanju manjih akustičkih jedinica govora – difona metodom TD-PSOLA. Za potrebe sustava izrađena je i baza difona za hrvatski govor. Predložen je automatski postupak odabira difona iz govornoga korpusa. Kvaliteta ostvarenoga postupka ispitana je provođenjem ankete među ispitanicima. Ispitanici su dali subjektivnu ocjenu kvalitete dobivenoga govora, a time je provjerena i njegova razumljivost.This paper presents the development of a Croatian text-to-speech system capable of synthesizing speech from arbitrary text. Input text in normalized form is first transcribed into a phonetic string (grapheme-to-phoneme conversion) and then processed by a TD-PSOLA based synthesizer. A procedure for automatic selection of diphones from a spoken corpus is proposed. A Croatian language diphone database was built for the system. Subjective quality evaluations of the resulting speech were performed, as well as tests for intelligibility

Évaluation expérimentale d'un système statistique de synthèse de la parole, HTS, pour la langue française

Les travaux présentés dans cette thèse se situent dans le cadre de la synthèse de la parole à partir du texte et, plus précisément, dans le cadre de la synthèse paramétrique utilisant des règles statistiques. Nous nous intéressons à l'influence des descripteurs linguistiques utilisés pour caractériser un signal de parole sur la modélisation effectuée dans le système de synthèse statistique HTS. Pour cela, deux méthodologies d'évaluation objective sont présentées. La première repose sur une modélisation de l'espace acoustique, généré par HTS par des mélanges gaussiens (GMM). En utilisant ensuite un ensemble de signaux de parole de référence, il est possible de comparer les GMM entre eux et ainsi les espaces acoustiques générés par les différentes configurations de HTS. La seconde méthodologie proposée repose sur le calcul de distances entre trames acoustiques appariées pour pouvoir évaluer la modélisation effectuée par HTS de manière plus locale. Cette seconde méthodologie permet de compléter les diverses analyses en contrôlant notamment les ensembles de données générées et évaluées. Les résultats obtenus selon ces deux méthodologies, et confirmés par des évaluations subjectives, indiquent que l'utilisation d'un ensemble complexe de descripteurs linguistiques n'aboutit pas nécessairement à une meilleure modélisation et peut s'avérer contre-productif sur la qualité du signal de synthèse produit.The work presented in this thesis is about TTS speech synthesis and, more particularly, about statistical speech synthesis for French. We present an analysis on the impact of the linguistic contextual factors on the synthesis achieved by the HTS statistical speech synthesis system. To conduct the experiments, two objective evaluation protocols are proposed. The first one uses Gaussian mixture models (GMM) to represent the acoustical space produced by HTS according to a contextual feature set. By using a constant reference set of natural speech stimuli, GMM can be compared between themselves and consequently acoustic spaces generated by HTS. The second objective evaluation that we propose is based on pairwise distances between natural speech and synthetic speech generated by HTS. Results obtained by both protocols, and confirmed by subjective evaluations, show that using a large set of contextual factors does not necessarily improve the modeling and could be counter-productive on the speech quality.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF