MSR-Codec: A Low-Bitrate Multi-Stream Residual Codec for High-Fidelity Speech Generation with Information Disentanglement
Abstract
Audio codecs are a critical component of modern speech generation systems. This paper introduces a low-bitrate, multi-scale residual codec that encodes speech into four distinct streams: semantic, timbre, prosody, and residual. This architecture achieves high-fidelity speech reconstruction at competitive low bitrates while demonstrating an inherent ability for information disentanglement. We construct a two-stage language model for text-to-speech (TTS) synthesis using this codec, which, despite its lightweight design and minimal data requirements, achieves a state-of-the-art Word Error Rate (WER) and superior speaker similarity compared to several larger models. Furthermore, the codec's design proves highly effective for voice conversion, enabling independent manipulation of speaker timbre and prosody.
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
This paper presents a novel low-bitrate multi-stream residual codec that effectively disentangles speech attributes for high-fidelity speech generation. The technical contributions are significant, with a well-structured methodology and robust experimental validation, positioning it as a valuable advancement in the field of audio processing and speech synthesis.
Methodology
The proposed method introduces a multi-stream residual codec that effectively disentangles speech into semantic, timbre, prosody, and residual streams. This architecture is innovative in its approach to information disentanglement, achieving high compression rates while maintaining speech quality. The use of pre-trained models for feature extraction and the cascaded architecture for stream fusion are well-justified and contribute to the overall efficiency of the codec. The methodology is clearly articulated, with a logical flow from speech encoding to reconstruction, and the integration of auxiliary losses enhances the model's ability to capture prosodic features.
Results & evaluation
The experiments are comprehensive, utilizing diverse datasets that ensure robustness in speaker identity and prosody representation. The results demonstrate the codec's competitive performance against existing models, achieving state-of-the-art WER and speaker similarity metrics. The inclusion of various evaluation metrics, such as STOI, PESQ, and WER, provides a well-rounded assessment of the model's capabilities. However, the paper could benefit from more extensive comparisons with a wider range of existing codecs to further validate its claims.
Reproducibility
The implementation details are described in sufficient depth, including the architecture of the codec and TTS model, as well as the training objectives and evaluation metrics. However, the lack of URLs for code repositories or demo pages limits the reproducibility of the work. Providing access to the code and trained models would significantly enhance the ability of other researchers to replicate the results.
Limitations
One limitation is the reliance on pre-trained models, which may not generalize well across different languages or dialects. Additionally, the evaluation is primarily focused on English datasets, potentially limiting the applicability of the findings to other languages. The paper also does not address the scalability of the model to larger datasets or more complex speech scenarios.
Broader impact
The proposed codec has significant implications for applications in speech synthesis, voice conversion, and other audio processing tasks. Its ability to disentangle speech attributes could lead to advancements in personalized TTS systems and more efficient audio streaming technologies. The lightweight design and low bitrate requirements make it particularly relevant for mobile and real-time applications, potentially broadening access to high-quality speech generation technologies. This paper presents a novel low-bitrate multi-stream residual codec that effectively disentangles speech attributes for high-fidelity speech generation. The technical contributions are significant, with a well-structured methodology and robust experimental validation, positioning it as a valuable advancement in the field of audio processing and speech synthesis.
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