toplogo
Sign In

A Novel Non-Autoregressive Framework (NAST-S2x) for End-to-End Simultaneous Speech-to-Speech Translation


Core Concepts
This paper introduces NAST-S2x, a novel non-autoregressive framework for end-to-end simultaneous speech-to-speech translation that overcomes limitations of traditional cascaded approaches by directly generating translations from speech input without relying on intermediate text representation, achieving high translation quality with low latency.
Abstract
  • Bibliographic Information: Ma, Z., Fang, Q., Zhang, S., Guo, S., Feng, Y., & Zhang, M. (2024). A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translation. arXiv preprint arXiv:2406.06937v2.
  • Research Objective: This paper aims to address the limitations of existing simultaneous speech-to-speech translation models, which often rely on cascaded approaches that suffer from error propagation and latency issues. The authors propose a novel non-autoregressive generation framework, NAST-S2x, to achieve end-to-end simultaneous speech-to-speech translation with improved efficiency and accuracy.
  • Methodology: NAST-S2x utilizes a chunk-based acoustic streaming encoder and a non-autoregressive streaming decoder. The decoder comprises linguistic and acoustic components, enabling direct generation of text or acoustic unit tokens from speech chunks. The model employs CTC decoding for dynamic latency adjustment and incorporates a two-step glancing and multi-task non-monotonic training strategy to enhance translation performance.
  • Key Findings: Experiments on MuST-C and CVSS-C datasets demonstrate NAST-S2x's effectiveness. In simultaneous speech-to-text translation, NAST-S2x achieves competitive or superior performance compared to state-of-the-art models. In simultaneous speech-to-speech translation, it outperforms cascaded approaches and achieves high-quality interpretation with low latency. Additionally, NAST-S2x demonstrates significant inference speedup in offline speech-to-speech translation.
  • Main Conclusions: NAST-S2x presents a promising solution for end-to-end simultaneous speech-to-speech translation. Its non-autoregressive nature and efficient training strategies contribute to its high performance and low latency. The proposed framework has the potential to enhance real-time communication across language barriers.
  • Significance: This research significantly contributes to the field of simultaneous speech-to-speech translation by proposing a novel and effective end-to-end framework. The model's ability to directly generate translations from speech input without relying on intermediate text representation addresses a key challenge in the field.
  • Limitations and Future Research: While NAST-S2x shows promising results, the authors acknowledge limitations regarding latency in speech-to-speech translation due to the reliance on an external vocoder. Future research could explore incorporating streaming-capable vocoders for further latency reduction. Additionally, the availability of parallel speech-to-speech translation corpora remains a challenge, and future work could focus on developing methods for generating or augmenting such data.
edit_icon

Customize Summary

edit_icon

Rewrite with AI

edit_icon

Generate Citations

translate_icon

Translate Source

visual_icon

Generate MindMap

visit_icon

Visit Source

Stats
The acoustic unit sequence is 25 times longer than the corresponding text sequence on average. NAST-S2x achieves high-quality simultaneous interpretation within a delay of less than 3 seconds. NAST-S2x provides a 28× decoding speedup in offline generation.
Quotes
"However, pipeline methods often suffer from error propagation and delay accumulation." "This characteristic allows it to be trained and tested directly from speech to acoustic units, thereby circumventing issues related to error propagation." "In Offline-S2S, it matches the performance of the strong autoregressive baseline while providing a 28× inference speedup."

Deeper Inquiries

0
star