TTA: Transcribe, Translate and Alignment for Cross-lingual Speech Representation
Abstract
Speech-LLM models have demonstrated great performance in multi-modal and multi-task speech understanding. A typical speech-LLM paradigm is integrating speech modality with a large language model (LLM). While the Whisper encoder was frequently adopted in previous studies for speech input, it shows limitations regarding input format, model scale, and semantic performance. To this end, we propose a lightweight TTA model specialized in speech semantics for more effective LLM integration. With large-scale training of 358k hours of speech data on multilingual speech recognition (ASR), speech translation (ST) and speech-text alignment tasks, TTA is capable of producing robust cross-lingual speech representations. Extensive evaluations across diverse benchmarks, including ASR/ST, speech retrieval, and ASR-LLM performance assessments, demonstrate TTA's superiority over Whisper. Furthermore, we rigorously validate the interplay between cross-lingual capabilities and ASR/ST performance. The model weights and training recipes of TTA will be released as part of an audio understanding toolkit Auden.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the TTA model, which enhances cross-lingual speech representation through a lightweight architecture and extensive training on multilingual datasets. This work addresses significant limitations in existing models and offers a promising avenue for future research in speech understanding and multilingual applications.
Methodology
The proposed TTA model introduces a lightweight architecture designed specifically for enhancing speech semantics in the context of cross-lingual integration with large language models. The methodology emphasizes large-scale training with 358k hours of multilingual speech data, which is a significant contribution to the field. The authors also address the limitations of the Whisper encoder, providing a clear rationale for their approach. However, the paper could benefit from a more detailed description of the model architecture and training procedures to enhance understanding.
Results & evaluation
The paper presents extensive evaluations across various benchmarks, including ASR, ST, and speech retrieval tasks, demonstrating the TTA model's superiority over Whisper. The results appear robust, but the paper lacks detailed statistical analysis of the results, which would strengthen the claims of superiority. The choice of benchmarks is appropriate, but additional comparative analyses with other state-of-the-art models could provide a broader context for the performance claims.
Reproducibility
The authors mention that they will release model weights and training recipes as part of an audio understanding toolkit, which is a positive step towards reproducibility. However, the paper does not provide enough implementation details or specific hyperparameters used during training, which could hinder other researchers from replicating the results effectively.
Limitations
One limitation is the reliance on the Whisper encoder's shortcomings without a thorough exploration of its strengths. Additionally, while the model is claimed to be lightweight, the paper does not provide a detailed analysis of its computational efficiency compared to Whisper or other models. The scope of multilingual capabilities also needs further clarification, as the paper does not specify which languages are supported or how performance varies across them.
Broader impact
The TTA model has the potential to significantly impact cross-lingual speech processing applications, enhancing multilingual communication and accessibility. Its integration with large language models could facilitate advancements in various domains, including education, customer service, and global collaboration. The release of the model weights and training recipes could foster further research and development in the field. The main contribution of this paper is the introduction of the TTA model, which enhances cross-lingual speech representation through a lightweight architecture and extensive training on multilingual datasets. This work addresses significant limitations in existing models and offers a promising avenue for future research in speech understanding and multilingual applications.
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