FuseCodec: Semantic-Contextual Fusion and Supervision for Neural Codecs
Abstract
Speech tokenization enables discrete representation and facilitates speech language modeling. However, existing neural codecs capture low-level acoustic features, overlooking the semantic and contextual cues inherent to human speech. While recent efforts introduced semantic representations from self-supervised speech models or incorporated contextual representations from pre-trained language models, challenges remain in aligning and unifying the semantic and contextual representations. We introduce FuseCodec, which unifies acoustic, semantic, and contextual representations through strong cross-modal alignment and globally informed supervision. We propose three complementary techniques: (i) Latent Representation Fusion, integrating semantic and contextual features directly into the encoder latent space for robust and unified representation learning; (ii) Global Semantic-Contextual Supervision, supervising discrete tokens with globally pooled and broadcasted representations to enhance temporal consistency and cross-modal alignment; and (iii) Temporally Aligned Contextual Supervision, strengthening alignment by dynamically matching contextual and speech tokens within a local window for fine-grained token-level supervision. We further introduce FuseCodec-TTS, demonstrating our methodology's applicability to zero-shot speech synthesis. Empirically, FuseCodec achieves state-of-the-art performance in LibriSpeech, surpassing EnCodec, SpeechTokenizer, and DAC in transcription accuracy, perceptual quality, intelligibility, and speaker similarity. Results highlight the effectiveness of contextually and semantically guided tokenization for speech tokenization and downstream tasks. Code and pretrained models are available at https://github.com/mubtasimahasan/FuseCodec.
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
FuseCodec introduces a novel framework for speech tokenization that effectively integrates acoustic, semantic, and contextual signals, significantly advancing the state of the art in speech processing. The combination of innovative methodologies and strong empirical results positions this work as a meaningful contribution to the field of machine learning and audio processing.
Methodology
The methodology proposed in FuseCodec is innovative, particularly in its approach to integrating semantic and contextual features into the encoder latent space. The three techniques—Latent Representation Fusion, Global Semantic-Contextual Supervision, and Temporally Aligned Contextual Supervision—are well-defined and address significant challenges in speech tokenization. The use of strong cross-modal alignment and globally informed supervision is a notable advancement, enhancing the robustness of the model. However, the paper could benefit from a more detailed explanation of the implementation specifics and how these techniques interact in practice.
Results & evaluation
The experiments conducted on the LibriSpeech dataset demonstrate the effectiveness of FuseCodec, achieving state-of-the-art results in multiple metrics such as transcription accuracy and perceptual quality. The comparison with existing models like EnCodec and SpeechTokenizer is thorough, showcasing clear improvements. However, the paper lacks a comprehensive analysis of the statistical significance of the results, which would strengthen the claims made about performance improvements.
Reproducibility
The availability of code and pretrained models on GitHub is a positive aspect, promoting reproducibility. However, the paper should include more detailed instructions on the setup and any dependencies required to run the experiments, as well as the specific configurations used for training and evaluation.
Limitations
One limitation is the potential overfitting to the LibriSpeech dataset, as the paper does not discuss generalization to other datasets or real-world applications. Additionally, while the methods are promising, the complexity of the model may pose challenges in deployment scenarios where computational resources are limited.
Broader impact
The implications of FuseCodec extend beyond speech tokenization, potentially impacting areas such as speech synthesis and natural language processing. The integration of semantic and contextual cues could enhance various applications, including virtual assistants, transcription services, and accessibility tools for the hearing impaired. The work encourages further exploration of multimodal approaches in audio processing, which could lead to more intuitive human-computer interactions. FuseCodec introduces a novel framework for speech tokenization that effectively integrates acoustic, semantic, and contextual signals, significantly advancing the state of the art in speech processing. The combination of innovative methodologies and strong empirical results positions this work as a meaningful contribution to the field of machine learning and audio processing.
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