222 research outputs found
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern,
high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely
on carefully hand-crafted lexicons developed by experts. This poses a two-fold
problem. Firstly, the lexicons are generated using a fixed phoneme set,
usually, ARPABET or IPA, which might not be the most optimal way to represent
phonemes for all languages. Secondly, the man-hours required to produce such an
expert lexicon are very high. In this paper, we eliminate both of these issues
by using recent advances in self-supervised learning to obtain data-driven
phoneme representations instead of fixed representations. We compare our
lexicon-free approach against strong baselines that utilize a well-crafted
lexicon. Furthermore, we show that our data-driven lexicon-free method performs
as good or even marginally better than the conventional rule-based or
lexicon-based neural G2Ps in terms of Mean Opinion Score (MOS) while using no
prior language lexicon or phoneme set, i.e. no linguistic expertise.Comment: Accepted at ICASSP 202
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Text-to-Text Transfer Transformer (T5) has recently been considered for the
Grapheme-to-Phoneme (G2P) transduction. As a follow-up, a tokenizer-free
byte-level model based on T5 referred to as ByT5, recently gave promising
results on word-level G2P conversion by representing each input character with
its corresponding UTF-8 encoding. Although it is generally understood that
sentence-level or paragraph-level G2P can improve usability in real-world
applications as it is better suited to perform on heteronyms and linking sounds
between words, we find that using ByT5 for these scenarios is nontrivial. Since
ByT5 operates on the character level, it requires longer decoding steps, which
deteriorates the performance due to the exposure bias commonly observed in
auto-regressive generation models. This paper shows that the performance of
sentence-level and paragraph-level G2P can be improved by mitigating such
exposure bias using our proposed loss-based sampling method.Comment: INTERSPEECH 202
Multi-Module G2P Converter for Persian Focusing on Relations between Words
In this paper, we investigate the application of end-to-end and multi-module
frameworks for G2P conversion for the Persian language. The results demonstrate
that our proposed multi-module G2P system outperforms our end-to-end systems in
terms of accuracy and speed. The system consists of a pronunciation dictionary
as our look-up table, along with separate models to handle homographs, OOVs and
ezafe in Persian created using GRU and Transformer architectures. The system is
sequence-level rather than word-level, which allows it to effectively capture
the unwritten relations between words (cross-word information) necessary for
homograph disambiguation and ezafe recognition without the need for any
pre-processing. After evaluation, our system achieved a 94.48% word-level
accuracy, outperforming the previous G2P systems for Persian.Comment: 10 pages, 4 figure
Towards a unified model for speech and language processing
Ce travail de recherche explore les méthodes d’apprentissage profond de la parole et du
langage, y inclus la reconnaissance et la synthèse de la parole, la conversion des graphèmes en
phonèmes et vice-versa, les modèles génératifs, visant de reformuler des tâches spécifiques dans
un problème plus général de trouver une représentation universelle d’information contenue
dans chaque modalité et de transférer un signal d’une modalité à une autre en se servant de
telles représentations universelles et à générer des représentations dans plusieurs modalités.
Il est compris de deux projets de recherche: 1) SoundChoice, un modèle graphème-phonème
tenant compte du contexte au niveau de la phrase qui réalise de bonnes performances et
des améliorations remarquables comparativement à un modèle de base et 2) MAdmixture, une
nouvelle approche pour apprendre des représentations multimodales dans un espace latent
commun.The present work explores the use of deep learning methods applied to a variety of areas
in speech and language processing including speech recognition, grapheme-to-phoneme conversion,
speech synthesis, generative models for speech and others to build toward a unified
approach that reframes these individual tasks into a more general problem of finding a
universal representation of information encoded in different modalities and being able to
seamlessly transfer a signal from one modality to another by converting it to this universal
representations and to generate samples in multiple modalities. It consists of two main
research projects: 1) SoundChocice, a context-aware sentence level Grapheme-to-Phoneme
model achieving solid performance on the task and a significant improvement on phoneme
disambiguation over baseline models and 2) MAdmixture, a novel approach to learning a variety
of speech representations in a common latent space
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