POTSA: A Cross-Lingual Speech Alignment Framework for Low Resource Speech-to-Text Translation
Abstract
Speech Large Language Models (SpeechLLMs) have achieved breakthroughs in multilingual speech-to-text translation (S2TT). However, existing approaches often overlook semantic commonalities across source languages, leading to biased translation performance. In this work, we propose \textbf{POTSA} (Parallel Optimal Transport for Speech Alignment), a new framework based on cross-lingual parallel speech pairs and Optimal Transport (OT), designed to bridge high- and low-resource translation gaps. First, we introduce a Bias Compensation module to coarsely align initial speech representations across languages. Second, we impose token-level OT constraints on a Q-Former using parallel speech pairs to establish fine-grained consistency of representations. Then, we apply a layer scheduling strategy to focus OT constraints on the most semantically beneficial layers. Experiments on the FLEURS dataset show that our method achieves SOTA performance, with +0.93 BLEU on average over five common languages and +5.05 BLEU on zero-shot languages, using only 10 hours of parallel speech per source language.
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 POTSA framework, which effectively enhances cross-lingual speech alignment for low-resource speech-to-text translation through innovative use of Optimal Transport and bias compensation techniques. This work significantly advances the field by addressing critical challenges in multilingual speech processing and demonstrates the potential for improved translation performance across diverse language pairs.
Methodology
The proposed POTSA framework introduces a novel approach to cross-lingual speech alignment using Optimal Transport (OT) principles. The methodology is well-structured, incorporating a Bias Compensation module to mitigate language-specific biases and a layer scheduling strategy to optimize the application of OT constraints. The use of parallel speech pairs for fine-grained alignment is innovative and addresses a significant gap in existing methodologies. The integration of these components demonstrates a thoughtful approach to improving multilingual speech-to-text translation.
Results & evaluation
The experiments conducted on the FLEURS dataset provide robust evidence of the framework's effectiveness, achieving state-of-the-art performance with significant BLEU score improvements, particularly in zero-shot scenarios. The ablation studies are well-designed, allowing for a clear understanding of the contributions of each component of the proposed method. The results are compelling and suggest that the proposed framework can indeed bridge the gap between high- and low-resource languages effectively.
Reproducibility
The paper provides sufficient implementation details, including the architecture of the SpeechLLM and the training setup, which enhances reproducibility. The authors have made their code publicly available, which is a positive aspect for the research community. However, the paper could benefit from more detailed descriptions of hyperparameter choices and training conditions to further facilitate reproducibility.
Limitations
While the proposed method shows promise, it relies on the availability of parallel speech pairs, which may not be feasible for all language pairs, especially in low-resource settings. Additionally, the performance gains, while significant, may still be limited by the underlying architecture of the SpeechLLM used, which could affect generalizability across other models. The paper does not discuss potential biases introduced by the selection of training data.
Broader impact
The implications of this research are substantial, particularly in enhancing multilingual communication and accessibility in low-resource languages. By improving speech-to-text translation capabilities, the framework could facilitate better integration of diverse languages in technology, potentially benefiting global communication and information dissemination. The approach could also inspire future research in cross-lingual representation learning and alignment methodologies. The main contribution of this paper is the introduction of the POTSA framework, which effectively enhances cross-lingual speech alignment for low-resource speech-to-text translation through innovative use of Optimal Transport and bias compensation techniques. This work significantly advances the field by addressing critical challenges in multilingual speech processing and demonstrates the potential for improved translation performance across diverse language pairs.
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