LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration (2412.15299v2)

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 LLMs. We expect this framework to serve as a cornerstone for flexible multilingual ASR systems that are generalizable even to unseen languages.

Insightful Overview of "LAMA-UT: Language Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration"

The paper presents a novel approach to multilingual automatic speech recognition (ASR) using a system called LAMA-UT, which stands for Language-Agnostic Multilingual ASR through Orthography Unification and Language-Specific Transliteration. The primary goal of this research is to address the complexities associated with developing a universal ASR model that performs equitably across a diverse array of languages, including low-resource and previously unseen languages.

Key Contributions

1. Pipeline Structure: LAMA-UT operates in two phases: first, a universal transcription generation phase, and second, a language-specific transliteration phase. The universal transcription generator unifies orthographic features via Romanization, thus capturing phonetic characteristics common across languages. This is followed by a universal converter, typically a LLM, transforming these transcriptions into language-specific outputs.
2. Minimal Reliance on Language-Specific Modules: One pivotal advancement of LAMA-UT is eschewing language-specific components like lexicons and LLMs during inference, unlike traditional multilingual ASR systems. This is critical for reducing system complexity and enhancing generalization to unseen languages.
3. Performance and Efficiency: The pipeline exhibits noteworthy performance metrics, achieving a 45% relative error reduction compared to Whisper, while being trained on merely 0.1% of Whisper's data size. This efficiency demonstrates the strong data utilization capability of the LAMA-UT framework.

Experimental Results and Claims

• Error Reduction and Data Efficiency: LAMA-UT outperforms several state-of-the-art models such as Whisper and is competitive with systems like MMS, despite its significantly reduced data requirement. This is indicative of the robustness of the Romanization approach combined with powerful LLMs in generating accurate transcriptions.
• Unseen Language Generalization: The pipeline's versatility is further demonstrated in its effective performance with unseen languages, matching zero-shot ASR techniques that traditionally require extensive language-specific modules.

Implications and Future Developments

The research positions LAMA-UT as a flexible framework capable of generalizing across multiple languages without relying heavily on specific language resources. This approach could transform how multilingual ASR systems are developed, especially when scaling models to accommodate a broader spectrum of languages with varying resource availability.

Future research directions may explore the integration of more sophisticated LLMs as universal converters to further minimize transcription errors, particularly for languages with complex phonetic or orthographic systems. Also, future advancements might include leveraging more nuanced linguistic features to enhance the accuracy of the universal transcription generator.

Conclusion

LAMA-UT represents a significant step forward in multilingual ASR, shifting towards more streamlined, language-agnostic approaches that leverage efficient data use and powerful LLMs. This work not only tackles immediate challenges in multilingual ASR but also sets the groundwork for future research and development aimed at scaling ASR technologies globally.