From Tens of Hours to Tens of Thousands: Scaling Back-Translation for Speech Recognition (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in Automatic Speech Recognition (ASR) have been fueled by massive speech corpora, but extending coverage to diverse languages with limited resources remains a formidable challenge. |
| Approach: | They propose a pipeline that converts large-scale text corpora into synthetic speech using off-the-shelf text-to-speech (TTS) models. |
| Outcome: | The proposed pipeline generates 500,000 hours of synthetic speech in ten languages and achieves transcription error reductions of over 30%. |
Similar Papers
CVSS Corpus and Massively Multilingual Speech-to-Speech Translation (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing work on speech-to-speech translation (S2ST) systems rely on text representation, but they are text-centric. |
| Approach: | They introduce a massively multilingual-to-English speech-tospeech translation corpus . they synthesize the translation text from the Common Voice speech corpus and CoVoST 2 into English . |
| Outcome: | The proposed corpus outperforms existing models on CoVoST 2 by 5.8 BLEU . the proposed model outperformed the previous state-of-the-art model without extra data . |
Hyper-BTS Dataset: Scalability and Enhanced Analysis of Back TranScription (BTS) for ASR Post-Processing (2024.findings-eacl)
Copied to clipboard
Chanjun Park, Jaehyung Seo, Seolhwa Lee, Junyoung Son, Hyeonseok Moon, Sugyeong Eo, Chanhee Lee, Heuiseok Lim
| Challenge: | Automatic Speech Recognition (ASR) post-processing requires substantial amounts of data, requiring expensive phonetic transcription experts. |
| Approach: | They propose a "Hyper-BTS" dataset that is five times larger than prior studies . they propose criteria for categorizing error types within ASR post-processing . |
| Outcome: | The proposed method can generate ASR inputs from clean text using a text-to-speech system. |
Back Translation for Speech-to-text Translation Without Transcripts (2023.acl-long)
Copied to clipboard
| Challenge: | End-to-end speech-totext translation (ST) is often achieved by utilizing source transcripts, but transcripts are only sometimes available since numerous unwritten languages exist worldwide. |
| Approach: | They propose an algorithm to synthesize pseudo ST data from monolingual target data to enhance ST without generating source transcripts. |
| Outcome: | The proposed method achieves an average boost of 2.3 BLEU on MuST-C En-De, En-Fr, and En-Es datasets. |
VoxpopuliTTS: a large-scale multilingual TTS corpus for zero-shot speech generation (2025.coling-main)
Copied to clipboard
Wenrui Liu, Jionghao Bai, Xize Cheng, Jialong Zuo, Ziyue Jiang, Shengpeng Ji, Minghui Fang, Xiaoda Yang, Qian Yang, Zhou Zhao
| Challenge: | Existing multilingual TTS datasets are limited in speech generation fields due to lack of quality data. |
| Approach: | They propose to use 30,000 hours of high-quality speech data across 3 languages . they filter out low-quality text-text pairs and concatenate short transcripts . |
| Outcome: | The proposed dataset comprises 30,000 hours of high-quality speech data, across 3 languages with multiple speakers and styles, suitable for various speech tasks such as TTS and ASR. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
Copied to clipboard
Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
Understanding Back-Translation at Scale (D18-1)
Copied to clipboard
| Challenge: | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. |
| Approach: | They propose to augment parallel training corpus with back-translations of target language sentences to improve neural machine translation with monolingual data. |
| Outcome: | The proposed method achieves a state-of-the-art of 35 BLEU on the WMT’14 English-German test set. |
Synthetic Doctor-Patient Dialogue Generation for Robust Medical ASR: A Scalable Pipeline for Vocabulary Expansion and Privacy Preservation (2026.eacl-industry)
Copied to clipboard
| Challenge: | Existing ASR models struggle with high word error rates (WER) on clinical vocabulary, especially medication names. |
| Approach: | They propose to generate doctor-patient dialogues in both text and audio formats using a curated set of over 124,000 medical terms. |
| Outcome: | The proposed pipeline generated over 1 billion audios with ground truth transcriptions. |
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation (N19-1)
Copied to clipboard
| Challenge: | Pre-training on high-resource automatic speech recognition (ASR) tasks improves ST performance even when source language is low-resourced. |
| Approach: | They propose a method to improve direct speech-to-text translation when source language is low-resource . they pre-train model on high-res automatic speech recognition task and fine-tune parameters for ST . |
| Outcome: | The proposed approach improves Spanish English ST even when the source language is low-resource . the pre-trained encoder accounts for most of the improvement, the authors show . |
A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
| Approach: | They propose to use unlabeled speech data to build multilingual ASR models that can be used for improved performance on low-resource languages. |
| Outcome: | The proposed models can be used to improve performance on low-resource languages by using unlabeled speech data. |
Leveraging Large Pre-trained Multilingual Models for High-Quality Speech-to-Text Translation on Industry Scenarios (2025.coling-main)
Copied to clipboard
| Challenge: | Speech-to-Text Translation systems rely on a sequential pipeline that combines ASR and MT models. |
| Approach: | They propose a parameter-efficient framework that integrates one LPSM with a multilingual MT engine. |
| Outcome: | The proposed framework integrates one LPSM with a multilingual MT engine. |