Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling

• URL: http://arxiv.org/abs/2209.15483v2
• Date: Mon, 29 May 2023 10:50:29 GMT
• Title: Augmentation Invariant Discrete Representation for Generative Spoken Language Modeling
• Authors: Itai Gat, Felix Kreuk, Tu Anh Nguyen, Ann Lee, Jade Copet, Gabriel Synnaeve, Emmanuel Dupoux, Yossi Adi
• Abstract summary: 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. 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.
• Score: 41.733860809136196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback [50.84142264245052]
  This work introduces the Align-SLM framework to enhance the semantic understanding of textless Spoken Language Models (SLMs) Our approach generates multiple speech continuations from a given prompt and uses semantic metrics to create preference data for Direct Preference Optimization (DPO) We evaluate the framework using ZeroSpeech 2021 benchmarks for lexical and syntactic modeling, the spoken version of the StoryCloze dataset for semantic coherence, and other speech generation metrics, including the GPT4-o score and human evaluation.
  arXiv  Detail & Related papers  (2024-11-04T06:07:53Z)
• LAST: Language Model Aware Speech Tokenization [24.185165710384997]
  We propose a novel approach to training a speech tokenizer by leveraging objectives from pre-trained textual LMs. Our aim is to transform features from a pre-trained speech model into a new feature space that enables better clustering for speech LMs.
  arXiv  Detail & Related papers  (2024-09-05T16:57:39Z)
• dMel: Speech Tokenization made Simple [19.169460770473908]
  We show that discretizing mel-filterbank channels into discrete intensity bins produces a simple representation (dMel) Our results demonstrate the effectiveness of dMel in achieving high performance on both tasks within a unified framework.
  arXiv  Detail & Related papers  (2024-07-22T17:51:53Z)
• SpeechGPT-Gen: Scaling Chain-of-Information Speech Generation [56.913182262166316]
  Chain-of-Information Generation (CoIG) is a method for decoupling semantic and perceptual information in large-scale speech generation. SpeechGPT-Gen is efficient in semantic and perceptual information modeling. It markedly excels in zero-shot text-to-speech, zero-shot voice conversion, and speech-to-speech dialogue.
  arXiv  Detail & Related papers  (2024-01-24T15:25:01Z)
• Bidirectional Representations for Low Resource Spoken Language Understanding [39.208462511430554]
  We propose a representation model to encode speech in bidirectional rich encodings. The approach uses a masked language modelling objective to learn the representations. We show that the performance of the resulting encodings is better than comparable models on multiple datasets.
  arXiv  Detail & Related papers  (2022-11-24T17:05:16Z)
• Modeling Intensification for Sign Language Generation: A Computational Approach [13.57903290481737]
  End-to-end sign language generation models do not accurately represent the prosody in sign language. We aim to improve the prosody in generated sign languages by modeling intensification in a data-driven manner. We find that our efforts in intensification modeling yield better results when evaluated with automatic metrics.
  arXiv  Detail & Related papers  (2022-03-18T01:13:21Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling [53.42814722621715]
  We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation. We show that this feature of our model improves the performance of the original BERT by large margins.
  arXiv  Detail & Related papers  (2020-10-30T13:33:41Z)
• Grounded Compositional Outputs for Adaptive Language Modeling [59.02706635250856]
  A language model's vocabulary$-$typically selected before training and permanently fixed later$-$affects its size. We propose a fully compositional output embedding layer for language models. To our knowledge, the result is the first word-level language model with a size that does not depend on the training vocabulary.
  arXiv  Detail & Related papers  (2020-09-24T07:21:14Z)
• Learning Spoken Language Representations with Neural Lattice Language Modeling [39.50831917042577]
  We propose a framework that trains neural lattice language models to provide contextualized representations for spoken language understanding tasks. The proposed two-stage pre-training approach reduces the demands of speech data and has better efficiency.
  arXiv  Detail & Related papers  (2020-07-06T10:38:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.