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

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

Towards the Automatic Processing of Language Registers: Semi-supervisedly Built Corpus and Classifier for French

International audienceLanguage registers are a strongly perceptible characteristic of texts and speeches. However, they are still poorly studied in natural language processing. In this paper, we present a semi-supervised approach which jointly builds a corpus of texts labeled in registers and an associated classifier. This approach relies on a small initial seed of expert data. After massively retrieving web pages, it iteratively alternates the training of an intermediate classifier and the annotation of new texts to augment the labeled corpus. The approach is applied to the casual, neutral, and formal registers, leading to a 750M word corpus and a final neural classifier with an acceptable performance

Towards the Automatic Processing of Language Registers: Semi-supervisedly Built Corpus and Classifier for French

International audienceLanguage registers are a strongly perceptible characteristic of texts and speeches. However, they are still poorly studied in natural language processing. In this paper, we present a semi-supervised approach which jointly builds a corpus of texts labeled in registers and an associated classifier. This approach relies on a small initial seed of expert data. After massively retrieving web pages, it iteratively alternates the training of an intermediate classifier and the annotation of new texts to augment the labeled corpus. The approach is applied to the casual, neutral, and formal registers, leading to a 750M word corpus and a final neural classifier with an acceptable performance

Production de paraphrases pour les systèmes vocaux humain-machine

This thesis focuses on the relationships between what is uttered and human-machine spoken dialogue systems that utter it. Instead of relying on all-purpose speech-synthesis engines, we consider that a message to synthesize is a variable that can be modified. As the primary characteristic of a message is its meaning, changing words so as to improve speech quality is allowable, provided that meaning is preserved. Performing such modifications is "paraphrase generation". This PhD thesis presents a study of statistical paraphrase generation for human-machine spoken dialogue systems. We first introduce to the design of a state-of-the-art paraphrase generator and an online evaluation platform. We then shed some light on some limitations of standard approaches to paraphrase generation and put forward an alternative model based on transformation rules. We show that usages of paraphrases must be taken into account during generation and evaluation, along with the meaning preservation criterion. At last, we introduce a new algorithm for paraphrase generation based on Monte-Carlo sampling and reinforcement learning. Studies of its behavior are reported. This algorithm overcomes some usual limitations of the Viterbi algorithm and paves the way for new paraphrase generation models.Cette thèse s'intéresse au lien entre ce qui est prononcé et le système vocal humaine-machine qui le prononce. Plutôt que de proposer des systèmes capables de tout vocaliser, nous envisageons le message comme une variable qui peut être modifiée. L'élément primordial d'un message est son sens. Il est donc possible de changer les mots utilisés si cela conserve le sens du message et améliore les systèmes vocaux. Cette modification s'appelle " production de paraphrases ". Dans cette thèse, nous proposons une étude de la production statistique de paraphrases pour les systèmes vocaux humain-machine. Pour ce faire, nous présentons la conception d'un système de référence et d'une plateforme d'évaluation en ligne. Nous mettons en lumière les différentes limites de l'approche classique et nous proposons un autre modèle fondé sur l'application de règles de transformation. Nous montrons qu'il est nécessaire de prendre en compte l'utilisation souhaitée des paraphrases lors de leur production et de leurs évaluations, pas uniquement du critère de conservation du sens. Enfin, nous proposons et étudions un nouvel algorithme pour produire des paraphrases, fondé sur l'échantillonnage de Monte- Carlo et l'apprentissage par renforcement. Cet algorithme permet de s'affranchir des contraintes habituelles de l'algorithme de Viterbi et donc de proposer librement de nouveaux modèles pour la paraphrase

Do not build your TTS training corpus randomly

International audienceTTS voice building generally relies on a script extracted from a big text corpus while optimizing the coverage of linguistic and phonological events supposedly related to voice acoustic quality. Previous works have shown differences on objective measures between smartly reduced and random corpora, but not when subjective evaluations are performed. For us, those results do not come from corpus reduction utility but from evaluations that smooth differences. In this article, we highlight those differences in a subjective test, by clustering test corpora according to a distance between signals so as to focus on different synthesized stimuli. The results show that covering appropriate features has a real impact on the perceived quality

Production de paraphrases pour les systèmes vocaux humain-machine

Cette thèse s intéresse au lien entre ce qui est prononcé et le système vocal humaine-machine qui le prononce. Plutôt que de proposer des systèmes capables de tout vocaliser, nous envisageons le message comme une variable qui peut être modifiée. L élément primordial d un message est son sens. Il est donc possible de changer les mots utilisés si cela conserve le sens du message et améliore les systèmes vocaux. Cette modification s appelle production de paraphrases . Dans cette thèse, nous proposons une étude de la production statistique de paraphrases pour les systèmes vocaux humain-machine. Pour ce faire, nous présentons la conception d un système de référence et d une plateforme d évaluation en ligne. Nous mettons en lumière les différentes limites de l approche classique et nous proposons un autre modèle fondé sur l application de règles de transformation. Nous montrons qu il est nécessaire de prendre en compte l utilisation souhaitée des paraphrases lors de leur production et de leurs évaluations, pas uniquement du critère de conservation du sens. Enfin, nous proposons et étudions un nouvel algorithme pour produire des paraphrases, fondé sur l échantillonnage de Monte-Carlo et l apprentissage par renforcement. Cet algorithme permet de s affranchir des contraintes habituelles de l algorithme de Viterbi et donc de proposer librement de nouveaux modèles pour la paraphrase.This thesis focuses on the relationships between what is uttered and human-machine spoken dialogue systems that utter it. Instead of relying on all-purpose speech-synthesis engines, we consider that a message to synthesize is a variable that can be modified. As the primary characteristic of a message is its meaning, changing words so as to improve speech quality is allowable, provided that meaning is preserved. Performing such modifications is "paraphrase generation". This PhD thesis presents a study of statistical paraphrase generation for human-machine spoken dialogue systems. We first introduce to the design of a state-of-the-art paraphrase generator and an online evaluation platform. We then shed some light on some limitations of standard approaches to paraphrase generation and put forward an alternative model based on transformation rules. We show that usages of paraphrases must be taken into account during generation and evaluation, along with the meaning preservation criterion. At last, we introduce a new algorithm for paraphrase generation based on Monte-Carlo sampling and reinforcement learning. Studies of its behavior are reported. This algorithm overcomes some usual limitations of the Viterbi algorithm and paves the way for new paraphrase generation models.CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

The True Score of Statistical Paraphrase Generation

International audienceThis article delves into the scoring function of the statistical paraphrase generation model. It presents an algorithm for exact computation and two applicative experiments. The first experiment analyses the behaviour of a statistical paraphrase generation decoder, and raises some issues with the ordering of n-best outputs. The second experiment shows that a major boost of performance can be obtained by embedding a true score computation inside a Monte-Carlo sampling based paraphrase generator