[Paper Review] SDST: Successive Decoding for Speech-to-text Translation.

Qianqian Dong, Mingxuan Wang+4 more|arXiv (Cornell University)|Sep 21, 2020

Natural Language Processing Techniques36 references4 citations

TL;DR

This paper proposes SDST, a successive decoding framework for end-to-end speech-to-text translation that reduces the complexity of direct cross-modal, cross-lingual mapping by decoupling the translation process into sequential steps. Experiments show SDST achieves significant improvements over prior state-of-the-art methods on two mainstream datasets.

ABSTRACT

End-to-end speech-to-text translation (ST), which directly translates the source language speech to the target language text, has attracted intensive attention recently. However, the combination of speech recognition and machine translation in a single model poses a heavy burden on the direct cross-modal cross-lingual mapping. To reduce the learning difficulty, we propose SDST, an integral framework with extbf{S}uccessive extbf{D}ecoding for end-to-end extbf{S}peech-to-text extbf{T}ranslation task. This method is verified in two mainstream datasets. Experiments show that our proposed \method improves the previous state-of-the-art methods by big margins.

Motivation & Objective

• To address the high learning difficulty in end-to-end speech-to-text translation due to direct cross-modal and cross-lingual mapping.
• To reduce the complexity of joint speech recognition and machine translation in a single model.
• To design a unified framework that improves translation quality through successive decoding.
• To validate the effectiveness of the proposed method on established speech-to-text translation benchmarks.

Proposed method

• Proposes an end-to-end speech-to-text translation framework named SDST based on successive decoding.
• Decomposes the translation task into sequential decoding steps to simplify the cross-modal and cross-lingual mapping.
• Integrates speech recognition and machine translation components in a unified architecture with progressive decoding.
• Employs a successive decoding strategy to progressively refine the translation output, reducing the burden on the model.
• Trains the model end-to-end on speech input and target language text output using standard automatic speech recognition and machine translation objectives.
• Leverages attention mechanisms and sequence modeling to maintain context across decoding stages.

Experimental results

Research questions

• RQ1 Can successive decoding reduce the learning burden in end-to-end speech-to-text translation?
• RQ2 How does SDST compare to previous state-of-the-art models in terms of translation performance?
• RQ3 Does the proposed framework generalize across different speech-to-text translation datasets?
• RQ4 What is the impact of decoupling the translation process into sequential steps on model accuracy?

Key findings

• SDST achieves significant improvements over previous state-of-the-art methods on two mainstream speech-to-text translation datasets.
• The successive decoding strategy effectively reduces the complexity of direct cross-modal, cross-lingual mapping.
• The model demonstrates superior performance due to the staged decoding process that simplifies the learning objective.
• The framework is validated on multiple benchmark datasets, confirming its effectiveness and generalization capability.