SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thought

SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thought

The paper "SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thought," authored by Hongyu Gong and Bandhav Veluri from Meta AI, presents an innovative approach to speech-to-speech translation (S2ST) that preserves both semantic content and speaker vocal style. This study focuses on overcoming the limitations of prior models which either required extensive style-aligned datasets or suffered from inefficiencies and error propagation due to a cascaded design.

Introduction

The field of Speech-to-Speech Translation (S2ST) is pivotal in enabling seamless cross-lingual verbal communication. Traditional encoder-decoder models have concentrated on semantic preservation, but have often neglected the acoustic nuances that convey speaker-specific vocal styles and emotions. Recent advances have leveraged language modeling (LM) to bypass the need for style-aligned data, using discrete speech representations and multiple speech LMs to encode both semantic and acoustic information. However, these multi-LM systems are computationally inefficient and prone to error propagation.

Proposed Model: SeamlessExpressiveLM

The centerpiece of this paper is the introduction of SeamlessExpressiveLM, a unified speech LM that combines semantic and acoustic generation in a single model using chain-of-thought prompting. This design decomposes the S2ST process into multiple intermediate generation steps, translating target semantic content and subsequently transferring speaker style to multi-stream acoustic units.

Key Contributions:

1. End-to-End S2ST Model: The model supports end-to-end processing with speaker style preservation, surpassing the performance of existing cascaded LMs in translation quality and parameter efficiency.
2. Training Data Economy: SeamlessExpressiveLM is trained solely on semantically aligned speech, eliminating the need for speaker style-aligned or speech-text aligned data.
3. Homogeneous Token Modeling: Through chain-of-thought prompting, the model effectively handles the heterogeneity between semantic and acoustic tokens.
4. Ablation Study Insights: The study explore how different prompt designs impact model performance, providing a nuanced understanding of S2ST modeling.

Model Architecture

SeamlessExpressiveLM is built on a decoder-only language model architecture. It incorporates:

• AR Layers: These are responsible for the autoregressive modeling of semantic units and initial acoustic units.
• NAR Layers: Non-autoregressive layers further model the multiple streams of acoustic units, facilitating efficient decoding.

The model utilizes discrete speech tokenizers such as HuBERT for semantic tokens and EnCodec for acoustic tokens. The chain-of-thought (CoT) approach enables sequential learning and reasoning from semantic translation to acoustic generation.

Experimental Results

The empirical evaluation focuses on Spanish-to-English (Es-En) and Hungarian-to-English (Hu-En) translations. SeamlessExpressiveLM demonstrates:

• Superior Vocal Style Transfer: Achieved higher vocal style similarity (VSim) scores than existing models.
• Comparable Semantic Quality: ASR-BLEU scores, a metric for semantic quality, were on par with other models.
• Parameter Efficiency: Outperformed cascaded LMs with fewer parameters.

Ablation Studies

Further experiments showed that eliminating chain-of-thought prompting or semantic prompts degraded performance, highlighting their critical role in maintaining translation integrity and efficiency. Another significant finding was the importance of the acoustic prompt ratio, which directly influenced the balance between semantic preservation and vocal style transfer.

Conclusion

The study successfully presents SeamlessExpressiveLM as a robust and efficient method for expressive S2ST. Its innovative use of chain-of-thought prompting integrates semantic and acoustic modeling within a unified framework, improving translation quality while reducing computational complexity. This paper marks a significant step toward achieving seamless and expressive speech translations without the heavy reliance on extensive style-aligned training data.

Future Directions

Potential research avenues include scaling the model and training data to enhance performance further and exploring multi-modal approaches (e.g., incorporating text data) to enrich the translation process.

Ethical Considerations

While promising, the deployment of SeamlessExpressiveLM must be carefully managed to avoid misuse in activities such as online scams, where voice impersonation could be exploited. Additionally, the system's accuracy and reliability need continuous monitoring to ensure high-quality translations in real-world applications.

Limitations

The model is restricted to speech-only data, and expanding to support various data types could bolster translation quality. Moreover, the experiments were limited in scope concerning model and data size, suggesting that larger-scale studies could yield even more significant improvements.

This paper provides a comprehensive and efficient solution to the challenges inherent in expressive S2ST, marking a considerable advancement in the field.