LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration

• URL: http://arxiv.org/abs/2412.15299v2
• Date: Mon, 23 Dec 2024 03:37:25 GMT
• Title: LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration
• Authors: Sangmin Lee, Woo-Jin Chung, Hong-Goo Kang,
• Abstract summary: We introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliteration (LAMA-UT) LAMA-UT operates without any language-specific modules while matching the performance of state-of-the-art models trained on a minimal amount of data. Our pipeline achieves a relative error reduction rate of 45% when compared to Whisper and performs comparably to MMS.
• Score: 19.403991814044424
• License:
• Abstract: Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties. To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliteration (LAMA-UT). LAMA-UT operates without any language-specific modules while matching the performance of state-of-the-art models trained on a minimal amount of data. Our pipeline consists of two key steps. First, we utilize a universal transcription generator to unify orthographic features into Romanized form and capture common phonetic characteristics across diverse languages. Second, we utilize a universal converter to transform these universal transcriptions into language-specific ones. In experiments, we demonstrate the effectiveness of our proposed method leveraging universal transcriptions for massively multilingual ASR. Our pipeline achieves a relative error reduction rate of 45% when compared to Whisper and performs comparably to MMS, despite being trained on only 0.1% of Whisper's training data. Furthermore, our pipeline does not rely on any language-specific modules. However, it performs on par with zero-shot ASR approaches which utilize additional language-specific lexicons and language models. We expect this framework to serve as a cornerstone for flexible multilingual ASR systems that are generalizable even to unseen languages.

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