Preserving Speaker Information in Direct Speech-to-Speech Translation with Non-Autoregressive Generation and Pretraining (2412.07316v2)

Abstract: Speech-to-Speech Translation (S2ST) refers to the conversion of speech in one language into semantically equivalent speech in another language, facilitating communication between speakers of different languages. Speech-to-Discrete Unit Translation (S2UT), a mainstream approach for end-to-end S2ST, addresses challenges such as error propagation across modules and slow inference speed often encountered in traditional cascade systems. However, as discrete units primarily capture content information, conventional S2UT methods fail to retain speaker-specific characteristics from the source. Our previous work, SC-S2UT, introduced a speaker adapter and a unit-to-mel structure, enabling the preservation of speaker information and non-autoregressive speech generation. Building on this foundation, this study proposes a self-supervised pretraining method to enrich the information extracted by both the speaker adapter and the unit-to-mel structure. Additionally, we investigate different feature fusion strategies to further improve the integration of speaker and content features. Experiments conducted on the CVSS-T dataset for ES-EN and FR-EN tasks demonstrate that our proposed method achieves a BLEU score improvement of 1.14 compared to SC-S2UT, along with significant enhancements in MOS and speaker similarity. Furthermore, our approach achieves translation quality comparable to traditional S2UT, with only a minimal increase of 0.04s per utterance in inference time, while maintaining high speaker similarity. These results validate the effectiveness of the proposed method.

• The paper introduces a non-autoregressive S2ST model that preserves speaker identity while boosting translation accuracy by 1.14 BLEU points.
• The study employs self-supervised pretraining to extract speaker-specific features without paired data, benefiting low-resource language pairs.
• The paper evaluates multiple fusion strategies and finds Cross-Attention optimal for integrating speaker and content features with high similarity.

Evaluating Advanced Speaker Information Preservation in Speech-to-Speech Translation

Recent advancements in speech-to-speech translation (S2ST) have prioritized the seamless integration of content and speaker characteristics to improve efficiency and accuracy. Traditional cascade S2ST systems often face limitations such as long inference times and error propagation through automatic speech recognition (ASR), machine translation (MT), and text-to-speech (TTS) modules. The paper "Preserving Speaker Information in Direct Speech-to-Speech Translation with Non-Autoregressive Generation and Pretraining" addresses these issues by proposing novel methods in the context of non-autoregressive (NAR) S2ST systems.

Non-Autoregressive Generation Optimizations

The paper explores non-autoregressive techniques that enhance both translation accuracy and speed. Traditional S2ST frameworks, particularly the speech-to-discrete unit translation (S2UT), prioritize content over speaker characteristics – a gap this paper aims to bridge. The introduction of a speaker adapter and unit-to-mel structure attempts to maintain speaker identity within an NAR framework, presenting a contrast to predictable losses in speaker fidelity that are inherent in conventional S2UT models. Improvement of BLEU scores by 1.14 points compared to prior work illustrates direct enhancements in translation quality.

Self-Supervised Pretraining

A significant contribution of this research is the implementation of self-supervised pretraining strategies. By using separate pretraining phases for speaker adapters and unit-to-mel structures, the researchers improve extraction and integration of speaker-specific features without the need for paired data. This novel approach reduces reliance on large labeled datasets and enhances performance, particularly in low-resource language pairs, as supported by experiments on the CVSS-T dataset for ES-EN and FR-EN tasks.

Fusion Methodologies for Feature Integration

Three distinct feature fusion strategies are evaluated: Cross-Attention, which dynamically aligns and integrates features; Gated Linear Unit (GLU), which regulates information flow; and Plus_FFN, which combines features through dimensionality reduction and nonlinear transformation. Among these, Cross-Attention demonstrated superior integration efficacy, leveraging adaptive attention weights for more accurate S2ST outputs. Such meticulous analysis of feature fusion offers robust methodologies for future S2ST frameworks aiming to balance speaker characteristics with linguistic content.

Comparative Performance Analysis

Distinct systems were benchmarked against the proposed methodologies, including contemporary cascade systems and end-to-end models like Style-S2UT. Results showcased competitive BLEU scores and significantly improved speaker similarity scores, aligning closely with ground truth similarities. Notably, the proposed non-autoregressive method, even with added enhancements for speaker embedding, only marginally increased inference overhead.

Implications and Future Directions

The implications of this research extend beyond immediate algorithmic improvements in S2ST systems. Practical applications may include multilingual communication platforms where preserving speaker identity enhances user experience and authenticity. The paper’s successful integration of self-supervised pretraining and non-autoregressive strategies signals potential developments in multilingual and multi-speaker contexts, possibly extending to unwritten languages or unique dialects.

Further exploration could investigate scalability across diverse languages, assessing the robustness of fusion strategies in complex linguistic scenarios. Additionally, future research might explore the integration of additional speaker characteristics, such as emotional tones or environmental contexts, enriching the quality of S2ST outputs.

In summary, this paper provides substantial contributions to the domain of speech-to-speech translation, particularly in preserving speaker identity without sacrificing translation speed or accuracy. It lays foundational work for groundbreaking enhancements in real-time translation systems, making it a significant addition to the field's evolving landscape.