Gabriel Synnaeve


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Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling
Itai Gat | Felix Kreuk | Tu Anh Nguyen | Ann Lee | Jade Copet | Gabriel Synnaeve | Emmanuel Dupoux | Yossi Adi
Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023)

Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LMs usually operate over discrete units obtained from quantizing internal representations of self-supervised models. Although such units show impressive modeling results, their robustness capabilities have not been extensively investigated. This work focuses on improving the robustness of discrete input representations for generative spoken language modeling. First, we formally define how to measure the robustness of such representations to various signal variations that do not alter the spoken information (e.g., time-stretch). Next, we empirically demonstrate how current state-of-the-art representation models lack robustness to such variations. To overcome this, we propose an effective and efficient method to learn robust discrete speech representation for generative spoken language modeling. The proposed approach is based on applying a set of signal transformations to the speech signal and optimizing the model using an iterative pseudo-labeling scheme. Our method significantly improves over the evaluated baselines when considering encoding and modeling metrics. We additionally evaluate our method on the speech-to-speech translation task, considering Spanish-English and French-English translations, and show the proposed approach outperforms the evaluated baselines.

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Generative Spoken Language Model based on continuous word-sized audio tokens
Robin Algayres | Yossi Adi | Tu Nguyen | Jade Copet | Gabriel Synnaeve | Benoît Sagot | Emmanuel Dupoux
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

In NLP, text language models based on words or subwords are known to outperform their character-based counterparts. Yet, in the speech community, the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter than a phoneme). Taking inspiration from word-based LM, we introduce a Generative Spoken Language Model (GSLM) based on word-size continuous-valued audio tokens that can generate diverse and expressive language output. This is obtained by replacing lookup table for lexical types with a Lexical Embedding function, the cross entropy loss by a contrastive loss, and multinomial sampling by k-NN sampling. The resulting model is the first generative language model based on word-size continuous tokens. Its performance is on par with discrete unit GSLMs regarding generation quality as measured by automatic metrics and subjective human judgements. Moreover, it is five times more memory efficient thanks to its large 200ms units. In addition, the embeddings before and after the Lexical Embedder are phonetically and semantically interpretable.


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Prosodic boundary information helps unsupervised word segmentation
Bogdan Ludusan | Gabriel Synnaeve | Emmanuel Dupoux
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Unsupervised Word Segmentation in Context
Gabriel Synnaeve | Isabelle Dautriche | Benjamin Börschinger | Mark Johnson | Emmanuel Dupoux
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers