Improving Direct Persian-English Speech-to-Speech Translation with Discrete Units and Synthetic Parallel Data

• URL: http://arxiv.org/abs/2511.12690v1
• Date: Sun, 16 Nov 2025 17:14:23 GMT
• Title: Improving Direct Persian-English Speech-to-Speech Translation with Discrete Units and Synthetic Parallel Data
• Authors: Sina Rashidi, Hossein Sameti,
• Abstract summary: Direct speech-to-speech translation (S2ST) models require large amounts of parallel speech data in the source and target languages.<n>This paper presents a direct S2ST system for translating Persian speech into English speech, as well as a pipeline for synthetic parallel Persian-English speech generation.
• Score: 1.3607388598209322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Direct speech-to-speech translation (S2ST), in which all components are trained jointly, is an attractive alternative to cascaded systems because it offers a simpler pipeline and lower inference latency. However, direct S2ST models require large amounts of parallel speech data in the source and target languages, which are rarely available for low-resource languages such as Persian. This paper presents a direct S2ST system for translating Persian speech into English speech, as well as a pipeline for synthetic parallel Persian-English speech generation. The model comprises three components: (1) a conformer-based encoder, initialized from self-supervised pre-training, maps source speech to high-level acoustic representations; (2) a causal transformer decoder with relative position multi-head attention translates these representations into discrete target speech units; (3) a unit-based neural vocoder generates waveforms from the predicted discrete units. To mitigate the data scarcity problem, we construct a new Persian-English parallel speech corpus by translating Persian speech transcriptions into English using a large language model and then synthesizing the corresponding English speech with a state-of-the-art zero-shot text-to-speech system. The resulting corpus increases the amount of available parallel speech by roughly a factor of six. On the Persian-English portion of the CVSS corpus, the proposed model achieves improvement of 4.6 ASR BLEU with the synthetic data over direct baselines. These results indicate that combining self-supervised pre-training, discrete speech units, and synthetic parallel data is effective for improving direct S2ST in low-resource language pairs such as Persian-English

Related papers

• RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data [30.27234062544891]
  This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot speech-to-speech translation (S2ST)<n>While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs.<n>Our model uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference.
  arXiv  Detail & Related papers  (2025-11-26T02:02:20Z)
• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer [53.72998363956454]
  Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy. The scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer during translation. We design an S2ST pipeline with style-transfer capability on the basis of discrete self-supervised speech representations and timbre units.
  arXiv  Detail & Related papers  (2023-09-14T09:52:08Z)
• Textless Direct Speech-to-Speech Translation with Discrete Speech Representation [27.182170555234226]
  We propose a novel model, Textless Translatotron, for training an end-to-end direct S2ST model without any textual supervision. When a speech encoder pre-trained with unsupervised speech data is used for both models, the proposed model obtains translation quality nearly on-par with Translatotron 2.
  arXiv  Detail & Related papers  (2022-10-31T19:48:38Z)
• Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation [94.80029087828888]
  Direct speech-to-speech translation (S2ST) is an attractive research topic with many advantages compared to cascaded S2ST. Direct S2ST suffers from the data scarcity problem because the corpora from speech of the source language to speech of the target language are very rare. We propose in this paper a Speech2S model, which is jointly pre-trained with unpaired speech and bilingual text data for direct speech-to-speech translation tasks.
  arXiv  Detail & Related papers  (2022-10-31T02:55:51Z)
• TranSpeech: Speech-to-Speech Translation With Bilateral Perturbation [61.564874831498145]
  TranSpeech is a speech-to-speech translation model with bilateral perturbation. We establish a non-autoregressive S2ST technique, which repeatedly masks and predicts unit choices. TranSpeech shows a significant improvement in inference latency, enabling speedup up to 21.4x than autoregressive technique.
  arXiv  Detail & Related papers  (2022-05-25T06:34:14Z)
• Enhanced Direct Speech-to-Speech Translation Using Self-supervised Pre-training and Data Augmentation [76.13334392868208]
  Direct speech-to-speech translation (S2ST) models suffer from data scarcity issues. In this work, we explore self-supervised pre-training with unlabeled speech data and data augmentation to tackle this issue.
  arXiv  Detail & Related papers  (2022-04-06T17:59:22Z)
• Textless Speech-to-Speech Translation on Real Data [49.134208897722246]
  We present a textless speech-to-speech translation (S2ST) system that can translate speech from one language into another language. We tackle the challenge in modeling multi-speaker target speech and train the systems with real-world S2ST data.
  arXiv  Detail & Related papers  (2021-12-15T18:56:35Z)

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.