PART: Progressive Alignment Representation Training for Multilingual Speech-To-Text with LLMs

Pei Zhang, Andong Chen, Xi Chen ...Full author list in the original paper.

The Chinese University of Hong Kong +3 · arXiv

Affiliations: The Chinese University of Hong Kong, Tongyi Lab, These authors contributed equally to this work and should, Alibaba Group

Paper

Abstract

Large language models (LLMs) have expanded from text to speech, giving rise to Speech Large Models (SLMs) that support recognition, translation, and synthesis. A key challenge is aligning speech and text representations, which becomes harder in multilingual settings. Existing methods often freeze LLM parameters and train encoders on multilingual data, but this forces cross-language convergence and limits performance. We introduce Progressive Alignment Representation Training (PART), a multi-stage and multi-task framework that separates within-language from cross-language alignment. During cross-language training, LLM parameters are dynamically activated, and text-based tasks are later introduced to enhance multilingual understanding. Experiments on CommonVoice 15, Fleurs, Wenetspeech, and CoVoST2 show that PART surpasses conventional approaches, with analysis confirming its ability to balance language-specific distinctions and cross-language generalization. These results demonstrate PART's effectiveness and generality for multilingual speech modality alignment.

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 PART framework, which effectively decouples intra-language alignment from cross-lingual alignment in multilingual speech processing, leading to improved performance in speech-to-text tasks. This work presents a substantial advancement in the methodology for training multilingual speech models, with clear implications for enhancing multilingual understanding in practical applications.

Methodology

The proposed PART framework introduces a multi-stage training approach that effectively separates within-language and cross-language alignment, which is a significant departure from conventional methods that often freeze LLM parameters. This progressive alignment strategy allows for dynamic activation of LLM parameters during cross-language tasks, enhancing the model's ability to leverage multilingual capabilities without forcing convergence of language-specific audio representations. The methodology is well-structured, with clear stages that build upon each other, and the introduction of text-based tasks in the final stage is a thoughtful addition that strengthens multilingual understanding.

Results & evaluation

The experiments conducted on multiple datasets (CommonVoice 15, Fleurs, Wenetspeech, and CoVoST2) demonstrate the effectiveness of the PART framework, showing significant performance improvements over traditional methods. The use of robust evaluation metrics such as WER and BLEU scores provides a clear indication of the model's capabilities in both ASR and S2TT tasks. The results are compelling, particularly in low-resource language pairs, highlighting the framework's generalizability and effectiveness across diverse multilingual scenarios.

Reproducibility

While the paper provides a detailed description of the methodology and experimental setup, it lacks specific URLs or repositories for code and data, which could hinder reproducibility. The absence of implementation details such as hyperparameters and model configurations also raises concerns about replicating the results. Including these elements would significantly enhance the paper's reproducibility.

Limitations

One limitation is the reliance on large-scale datasets, which may not be readily available for all languages, potentially limiting the applicability of the proposed method in low-resource settings. Additionally, while the progressive unfreezing strategy shows promise, it may require careful tuning to achieve optimal results, which could complicate the training process for practitioners.

Broader impact

The PART framework has the potential to significantly advance the field of multilingual speech processing by improving the performance of SLMs in recognizing and translating speech across languages. This could lead to more effective communication tools and applications in diverse linguistic contexts, ultimately enhancing accessibility and inclusivity in technology. The main contribution of this paper is the introduction of the PART framework, which effectively decouples intra-language alignment from cross-lingual alignment in multilingual speech processing, leading to improved performance in speech-to-text tasks. This work presents a substantial advancement in the methodology for training multilingual speech models, with clear implications for enhancing multilingual understanding in practical applications.

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