Newest papers

Monsoon: Scalable Spontaneous Speech Collection for Low-Resource ASR in 50 Languages

Shobhit Banga, Manas Dhir, Aaditya Pareek, Amritansh Walecha, Bhaskar Singh, Hanuman Sidh, Manmeet Kaur, Sagar C. Jain, Supriya Paul, Vanshika Chhabra, Utkarsh Singh · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26776 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

ほとんどの言語では、自動音声認識(ASR)はモデルアーキテクチャによって制限されることが少なく、正確に翻訳された自発的な発言の欠如によって制限されています。我々は、そのようなデータを規模で収集するためのフレームワークであるモンソンを紹介します。それは、並行した貢献者間の未スクリプトな会話を引き起こし、自動分類器でスクリーンレコーディングを表示し、人間のレビューの5つのレベルで翻訳を訂正し、レクシカルおよび音響的なカバーのためのセグメントを選択します。それは50の言語で収集されたスピーチを持っています。我々は7つの3500時間のコルポーラを分析します。同等なボリュームで、その品質段階

Machine translation (Meta M2M100); the English original follows.

Abstract

For most languages, automatic speech recognition (ASR) is limited less by model architecture than by a lack of accurately transcribed spontaneous speech. We present Monsoon, a framework for collecting such data at scale. It elicits unscripted conversations between paired contributors, screens recordings with automated classifiers, corrects transcripts over five levels of human review, and selects segments for lexical and acoustic coverage. It has collected speech in 50 languages; we analyse seven 3,500-hour corpora. At equal volume, its quality stages reduce semantic word error rate (WER) by 10-39% relative to a conventional protocol. Compared with an architecture-matched baseline trained on public corpora, Whisper fine-tuned on Monsoon performs comparably on the baseline's own test sets and reduces semantic WER by 20-44 points on conversational benchmarks. The gains transfer to a second architecture and grow with data volume. We release the framework, its quality-control models, and the fine-tuned models.

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Topics: Computer science, Speech recognition, Natural language processing, Word error rate, Artificial intelligence, Word (group theory)

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