Transformation de l'intonation : application à la synthèse de la parole et à la transformation de voix

The work presented in this thesis lies within the scope of prosody conversion and more particularly the fundamental frequency conversion which is considered as a prominent factor in prosody processing. This document deals with the different steps necessary to build such a conversion system : stylization, clustering and conversion of melodic contours. For each step, we propose a methodology that takes into account the issues and difficulties encountered in the previous one. A B-spline based approach is first proposed to model the melodic contours. Then to represent the melodic space of a speaker, a HMM based approach is introduced. To finish, a prosody transformation methodology using non-parallel corpora based on a speaker adaptation technique is derived. The results we obtain tend to show that it is necessary to model the evolution of the melody and to drive the transformation system by using morpho-syntactic information.Les travaux de cette thèse se situent dans le cadre de la transformation de la prosodie en se focalisant sur la fréquence fondamentale, F0, facteur jugé proéminent dans le traitement de la prosodie. En particulier, nous nous intéressons aux différentes étapes nécessaires à la construction d'un tel système : la stylisation, la classification et la transformation des contours mélodiques. Pour chaque étape, nous proposons une méthodologie qui tient compte des problèmes qui se sont posés à l'étape précédente. Tout d'abord, un modèle B-spline est proposé pour la stylisation des contours mélodiques. Ensuite, pour représenter l'espace mélodique du locuteur, une approche par modèles de Markov est introduite. Enfin, une méthodologie de transformation de la prosodie à partir de corpus non parallèles par une technique d'adaptation au locuteur est présentée. Les résultats obtenus tendent à montrer qu'il est nécessaire de traiter la dynamique du F0 et de piloter la transformation par des informations d'ordre morphosyntaxique

Adaptive Statistical Utterance Phonetization for French

International audienceTraditional utterance phonetization methods concatenate pronunciations of uncontextualized constituent words. This approach is too weak for some languages, like French, where transitions between words imply pronunciation modifications. Moreover, it makes it difficult to consider global pronunciation strategies, for instance to model a specific speaker or a specific accent. To overcome these problems, this paper presents a new original phonetization approach for French to generate pronunciation variants of utterances. This approach offers a statistical and highly adaptive framework by relying on conditional random fields and weighted finite state transducers. The approach is evaluated on a corpus of isolated words and a corpus of spoken utterances

Phonétisation statistique adaptable d'énoncés pour le français

International audienceTraditional utterance phonetization methods concatenate pronunciations of uncontextualized constituentwords. This approach is too weak for some languages, like French, where transitions betweenwords imply pronunciation modifications. Moreover, it makes it difficult to consider global pronunciationstrategies, for instance to model a specific speaker or a specific accent. To overcome theseproblems, this paper presents a new original phonetization approach for French to generate pronunciationvariants of utterances. This approach offers a statistical and highly adaptive framework byrelying on conditional random fields and weighted finite state transducers. The approach is evaluatedon a corpus of isolated words and a corpus of spoken utterances.Les méthodes classiques de phonétisation d'énoncés concatènent les prononciations hors-contexte des mots. Ce type d'approches est trop faible pour certaines langues, comme le français, où les transitions entre les mots impliquent des modifications de prononciation. De plus, cela rend difficile la modélisation de stratégies de prononciation globales, par exemple pour modéliser un locuteur ou un accent particulier. Pour palier ces problèmes, ce papier présente une approche originale pour la phonétisation du français afin de générer des variantes de prononciation dans le cas d'énoncés. Par l'emploi de champs aléatoires conditionnels et de transducteurs finis pondérés, cette approche propose un cadre statistique particulièrement souple et adaptable. Cette approche est évaluée sur un corpus de mots isolés et sur un corpus d'énoncés prononcés

Corpus design for expressive speech: impact of the utterance length

International audienceVoice corpus plays a crucial role in the quality of the synthetic speech generation, specially under a length constraint. Creating a new voice is costly and the recording script selection for an expressive TTS task is generally considered as an optimization problem in order to achieve a rich and parsimonious corpus. In order to vocalize a given book using a TTS system, we investigate four script selection approaches. Based on preliminary observations, we simply propose to select shortest utterances of the book and compare the achievements of this method with state of the art ones for two books, with different utterance lengths and styles, using two kinds of concatenation based TTS systems. The study of the TTS costs indicates that selecting the shortest utterances could result in better synthetic quality, which is confirmed by a perceptual test. By investigating usual criteria for corpus design in literature like unit coverage or distribution similarity of units, it turns out that they are not pertinent metrics in the framework of this study

Style versus Content: A distinction without a (learnable) difference?

International audienceTextual style transfer involves modifying the style of a text while preserving its content. This assumes that it is possible to separate style from content. This paper investigates whether this separation is possible. We use sentiment transfer as our case study for style transfer analysis. Our experimental methodology frames style transfer as a multi-objective problem, balancing style shift with content preservation and fluency. Due to the lack of parallel data for style transfer we employ a variety of adversarial encoder-decoder networks in our experiments. Also, we use a probing methodology to analyse how these models encode style-related features in their latent spaces. The results of our experiments which are further confirmed by a human evaluation reveal an inherent trade-off between the multiple style transfer objectives and indicate that style cannot be usefully separated from content within these style-transfer systems

Neural-Driven Multi-criteria Tree Search for Paraphrase Generation

International audienceA good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity. To deal with this tradeoff, we propose to cast the paraphrase generation task as a multi-objectives search problem on the lattice of text transformations. We use BERT and GPT2 to measure respectively the semantic distance and the correctness of the candidates. We study two search algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS) that we use to explore the huge sets of candidates generated by applying the PPDB-2.0 edition rules. We evaluate this approach on 5 datasets and show that it performs reasonably well and that it outperforms a state-of-the-art edition-based text generation method

Improving TTS with corpus-specific pronunciation adaptation

International audienceText-to-speech (TTS) systems are built on speech corpora which are labeled with carefully checked and segmented phonemes. However, phoneme sequences generated by automatic grapheme-to-phoneme converters during synthesis are usually inconsistent with those from the corpus, thus leading to poor quality synthetic speech signals. To solve this problem , the present work aims at adapting automatically generated pronunciations to the corpus. The main idea is to train corpus-specific phoneme-to-phoneme conditional random fields with a large set of linguistic, phonological, articulatory and acoustic-prosodic features. Features are first selected in cross-validation condition, then combined to produce the final best feature set. Pronunciation models are evaluated in terms of phoneme error rate and through perceptual tests. Experiments carried out on a French speech corpus show an improvement in the quality of speech synthesis when pronunciation models are included in the phonetization process. Appart from improving TTS quality, the presented pronunciation adaptation method also brings interesting perspectives in terms of expressive speech synthesis

On the suitability of vocalic sandwiches in a corpus-based TTS engine

International audienceUnit selection speech synthesis systems generally rely on target and concatenation costs for selecting the best unit sequence. The role of the concatenation cost is to insure that joining two voice segments will not cause any acoustic artefact to appear. For this task, acoustic distances (MFCC, F0) are typically used but in many cases, this is not enough to prevent concatenation artefacts. Among other strategies, the improvement of corpus covering by favouring units that naturally support well the joining process (vocalic sandwiches) seems to be effective on TTS. In this paper, we investigate if vocalic sandwiches can be used directly in the unit selection engine when the corpus was not created using that principle. First, the sandwich approach is directly transposed in the unit selection engine with a penalty that greatly favours concatenation on sandwich boundaries. Second, a derived fuzzy version is proposed to relax the penalty based on the concatenation cost, with respect to the cost distribution. We show that the sandwich approach, very efficient at the corpus creation step, seems to be inefficient when directly transposed in the unit selection engine. However, we observe that the fuzzy approach enhances synthesis quality, especially on sentences with high concatenation costs

Phonétisation statistique adaptable d'énoncés pour le français

International audienceTraditional utterance phonetization methods concatenate pronunciations of uncontextualized constituentwords. This approach is too weak for some languages, like French, where transitions betweenwords imply pronunciation modifications. Moreover, it makes it difficult to consider global pronunciationstrategies, for instance to model a specific speaker or a specific accent. To overcome theseproblems, this paper presents a new original phonetization approach for French to generate pronunciationvariants of utterances. This approach offers a statistical and highly adaptive framework byrelying on conditional random fields and weighted finite state transducers. The approach is evaluatedon a corpus of isolated words and a corpus of spoken utterances.Les méthodes classiques de phonétisation d'énoncés concatènent les prononciations hors-contexte des mots. Ce type d'approches est trop faible pour certaines langues, comme le français, où les transitions entre les mots impliquent des modifications de prononciation. De plus, cela rend difficile la modélisation de stratégies de prononciation globales, par exemple pour modéliser un locuteur ou un accent particulier. Pour palier ces problèmes, ce papier présente une approche originale pour la phonétisation du français afin de générer des variantes de prononciation dans le cas d'énoncés. Par l'emploi de champs aléatoires conditionnels et de transducteurs finis pondérés, cette approche propose un cadre statistique particulièrement souple et adaptable. Cette approche est évaluée sur un corpus de mots isolés et sur un corpus d'énoncés prononcés