# Improving Stability in Simultaneous Speech Translation: A Revision-Controllable Decoding Approach

arXiv: https://arxiv.org/abs/2310.04399 · HTML: https://mlsanity.com/paper/2310.04399/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss](https://mlsanity.com/paper/2002.02562/) (2020)
- [Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset](https://mlsanity.com/paper/2010.11395/) (2020)
- [Transformer-Transducer: End-to-End Speech Recognition with Self-Attention](https://mlsanity.com/paper/1910.12977/) (2019)
- [Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR](https://mlsanity.com/paper/2106.06636/) (2021)
- [Large-Scale Streaming End-to-End Speech Translation with Neural Transducers](https://mlsanity.com/paper/2204.05352/) (2022)
- [Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation](https://mlsanity.com/paper/1904.07209/) (2019)
- [Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation](https://mlsanity.com/paper/1912.03393/) (2019)
- [Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings](https://mlsanity.com/paper/2010.11247/) (2020)
- [Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks](https://mlsanity.com/paper/2305.03101/) (2023)
- [A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability](https://mlsanity.com/paper/2211.02499/) (2022)
- [Speculative Beam Search for Simultaneous Translation](https://mlsanity.com/paper/1909.05421/) (2019)
- [Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training](https://mlsanity.com/paper/2010.10048/) (2020)
- [Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments](https://mlsanity.com/paper/2307.03354/) (2023)

## Built on by (most cited first, 2 of 2)

- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Recent Advances in End-to-End Simultaneous Speech Translation](https://mlsanity.com/paper/2406.00497/) (2024)

## Abstract (arXiv)

Simultaneous Speech-to-Text translation serves a critical role in real-time crosslingual communication. Despite the advancements in recent years, challenges remain in achieving stability in the translation process, a concern primarily manifested in the flickering of partial results. In this paper, we propose a novel revision-controllable method designed to address this issue. Our method introduces an allowed revision window within the beam search pruning process to screen out candidate translations likely to cause extensive revisions, leading to a substantial reduction in flickering and, crucially, providing the capability to completely eliminate flickering. The experiments demonstrate the proposed method can significantly improve the decoding stability without compromising substantially on the translation quality.