BhasaAnuvaad: A Speech Translation Dataset for 13 Indian Languages

• URL: http://arxiv.org/abs/2411.04699v2
• Date: Fri, 08 Nov 2024 14:29:03 GMT
• Title: BhasaAnuvaad: A Speech Translation Dataset for 13 Indian Languages
• Authors: Sparsh Jain, Ashwin Sankar, Devilal Choudhary, Dhairya Suman, Nikhil Narasimhan, Mohammed Safi Ur Rahman Khan, Anoop Kunchukuttan, Mitesh M Khapra, Raj Dabre,
• Abstract summary: We evaluate the performance of widely-used Automatic Speech Translation systems on Indian languages. There is a striking absence of systems capable of accurately translating colloquial and informal language. We introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 13 out of 22 scheduled Indian languages and English.
• Score: 27.273651323572786
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatic Speech Translation (AST) datasets for Indian languages remain critically scarce, with public resources covering fewer than 10 of the 22 official languages. This scarcity has resulted in AST systems for Indian languages lagging far behind those available for high-resource languages like English. In this paper, we first evaluate the performance of widely-used AST systems on Indian languages, identifying notable performance gaps and challenges. Our findings show that while these systems perform adequately on read speech, they struggle significantly with spontaneous speech, including disfluencies like pauses and hesitations. Additionally, there is a striking absence of systems capable of accurately translating colloquial and informal language, a key aspect of everyday communication. To this end, we introduce BhasaAnuvaad, the largest publicly available dataset for AST involving 13 out of 22 scheduled Indian languages and English spanning over 44,400 hours and 17M text segments. BhasaAnuvaad contains data for English speech to Indic text, as well as Indic speech to English text. This dataset comprises three key categories: (1) Curated datasets from existing resources, (2) Large-scale web mining, and (3) Synthetic data generation. By offering this diverse and expansive dataset, we aim to bridge the resource gap and promote advancements in AST for Indian languages.

Related papers

• Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection [49.27067541740956]
  Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. Building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. We propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages.
  arXiv  Detail & Related papers  (2024-09-17T08:36:45Z)
• IndicVoices-R: Unlocking a Massive Multilingual Multi-speaker Speech Corpus for Scaling Indian TTS [0.9092013845117769]
  IndicVoices-R (IV-R) is the largest multilingual Indian TTS dataset derived from an ASR dataset. IV-R matches the quality of gold-standard TTS datasets like LJ,Speech LibriTTS, and IndicTTS. We release the first TTS model for all 22 official Indian languages.
  arXiv  Detail & Related papers  (2024-09-09T06:28:47Z)
• Navigating Text-to-Image Generative Bias across Indic Languages [53.92640848303192]
  This research investigates biases in text-to-image (TTI) models for the Indic languages widely spoken across India. It evaluates and compares the generative performance and cultural relevance of leading TTI models in these languages against their performance in English.
  arXiv  Detail & Related papers  (2024-08-01T04:56:13Z)
• CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving [61.73180469072787]
  We focus on the problem of spoken translation (ST) of code-switched speech in Indian languages to English text. We present a new end-to-end model architecture COSTA that scaffolds on pretrained automatic speech recognition (ASR) and machine translation (MT) modules. COSTA significantly outperforms many competitive cascaded and end-to-end multimodal baselines by up to 3.5 BLEU points.
  arXiv  Detail & Related papers  (2024-06-16T16:10:51Z)
• Wav2Gloss: Generating Interlinear Glossed Text from Speech [78.64412090339044]
  We propose Wav2Gloss, a task in which four linguistic annotation components are extracted automatically from speech. We provide various baselines to lay the groundwork for future research on Interlinear Glossed Text generation from speech.
  arXiv  Detail & Related papers  (2024-03-19T21:45:29Z)
• IndicVoices: Towards building an Inclusive Multilingual Speech Dataset for Indian Languages [17.862027695142825]
  INDICVOICES is a dataset of natural and spontaneous speech from 16237 speakers covering 145 Indian districts and 22 languages. 1639 hours have already been transcribed, with a median of 73 hours per language. All the data, tools, guidelines, models and other materials developed as a part of this work will be made publicly available.
  arXiv  Detail & Related papers  (2024-03-04T10:42:08Z)
• Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning [49.79783940841352]
  Existing datasets are almost all in the English language. We work with fluent speakers of languages from around the world to collect natural instances of instructions and completions. We create the most extensive multilingual collection to date, comprising 513 million instances through templating and translating existing datasets across 114 languages.
  arXiv  Detail & Related papers  (2024-02-09T18:51:49Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Breaking Language Barriers: A Question Answering Dataset for Hindi and Marathi [1.03590082373586]
  This paper focuses on developing a Question Answering dataset for two such languages- Hindi and Marathi. Despite Hindi being the 3rd most spoken language worldwide, and Marathi being the 11th most spoken language globally, both languages face limited resources for building efficient Question Answering systems. We release the largest Question-Answering dataset available for these languages, with each dataset containing 28,000 samples.
  arXiv  Detail & Related papers  (2023-08-19T00:39:21Z)
• Neural Machine Translation for the Indigenous Languages of the Americas: An Introduction [102.13536517783837]
  Most languages from the Americas are among them, having a limited amount of parallel and monolingual data, if any. We discuss the recent advances and findings and open questions, product of an increased interest of the NLP community in these languages.
  arXiv  Detail & Related papers  (2023-06-11T23:27:47Z)
• IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages [37.758476568195256]
  India has a rich linguistic landscape with languages from 4 major language families spoken by over a billion people. 22 of these languages are listed in the Constitution of India (referred to as scheduled languages)
  arXiv  Detail & Related papers  (2023-05-25T17:57:43Z)
• ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text Translation [79.66359274050885]
  We present ComSL, a speech-language model built atop a composite architecture of public pretrained speech-only and language-only models. Our approach has demonstrated effectiveness in end-to-end speech-to-text translation tasks.
  arXiv  Detail & Related papers  (2023-05-24T07:42:15Z)
• PMIndiaSum: Multilingual and Cross-lingual Headline Summarization for Languages in India [33.31556860332746]
  PMIndiaSum is a multilingual and massively parallel summarization corpus focused on languages in India. Our corpus provides a training and testing ground for four language families, 14 languages, and the largest to date with 196 language pairs.
  arXiv  Detail & Related papers  (2023-05-15T17:41:15Z)
• Beyond English-Centric Multilingual Machine Translation [74.21727842163068]
  We create a true Many-to-Many multilingual translation model that can translate directly between any pair of 100 languages. We build and open source a training dataset that covers thousands of language directions with supervised data, created through large-scale mining. Our focus on non-English-Centric models brings gains of more than 10 BLEU when directly translating between non-English directions while performing competitively to the best single systems of WMT.
  arXiv  Detail & Related papers  (2020-10-21T17:01:23Z)
• XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning [68.57658225995966]
  Cross-lingual Choice of Plausible Alternatives (XCOPA) is a typologically diverse multilingual dataset for causal commonsense reasoning in 11 languages. We evaluate a range of state-of-the-art models on this novel dataset, revealing that the performance of current methods falls short compared to translation-based transfer.
  arXiv  Detail & Related papers  (2020-05-01T12:22:33Z)
• CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus [57.641761472372814]
  CoVoST is a multilingual speech-to-text translation corpus from 11 languages into English. It diversified with over 11,000 speakers and over 60 accents. CoVoST is released under CC0 license and free to use.
  arXiv  Detail & Related papers  (2020-02-04T14:35:28Z)

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.