Semantic Codebooks as Effective Priors for Neural Speech Compression
Abstract
Speech codecs are traditionally optimized for waveform fidelity, allocating bits to preserve acoustic detail even when much of it can be inferred from linguistic structure. This leads to inefficient compression and suboptimal performance on downstream recognition tasks. We propose SemDAC, a semantic-aware neural audio codec that leverages semantic codebooks as effective priors for speech compression. In SemDAC, the first quantizer in a residual vector quantization (RVQ) stack is distilled from HuBERT features to produce semantic tokens that capture phonetic content, while subsequent quantizers model residual acoustics. A FiLM-conditioned decoder reconstructs audio conditioned on the semantic tokens, improving efficiency in the use of acoustic codebooks. Despite its simplicity, this design proves highly effective: SemDAC outperforms DAC across perceptual metrics and achieves lower WER when running Whisper on reconstructed speech, all while operating at substantially lower bitrates (e.g., 0.95 kbps vs. 2.5 kbps for DAC). These results demonstrate that semantic codebooks provide an effective inductive bias for neural speech compression, producing compact yet recognition-friendly representations.
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
The main contribution of this paper is the introduction of SemDAC, a semantic-aware neural audio codec that effectively integrates semantic codebooks into the speech compression process, leading to improved efficiency and recognition accuracy. This work represents a meaningful advancement in the field of audio processing, particularly in the context of neural codecs, by demonstrating the importance of semantic information in enhancing audio quality at lower bitrates.
Methodology
The proposed methodology in SemDAC is innovative, as it integrates semantic codebooks into the neural audio codec framework, specifically leveraging HuBERT features to enhance the quantization process. The use of a FiLM-conditioned decoder to incorporate semantic tokens into the reconstruction process is a significant advancement over traditional approaches that primarily focus on acoustic fidelity. The asymmetric design of the quantization layers, separating semantic and acoustic representations, is a thoughtful approach that addresses the inefficiencies of existing codecs.
Results & evaluation
The experiments are well-structured, utilizing the LibriSpeech dataset, which is a standard benchmark in speech processing. The comparison against the DAC and Opus codecs across various bitrates provides a comprehensive evaluation of SemDAC's performance. The results demonstrate clear advantages in perceptual metrics and word error rates, particularly at lower bitrates, which is crucial for practical applications in speech compression.
Reproducibility
The paper provides sufficient details regarding the model architecture, training procedures, and evaluation metrics, which should facilitate reproducibility. However, the absence of a public code repository or demo URL limits the practical reproducibility of the results, as other researchers would need to implement the model from scratch based on the provided descriptions.
Limitations
While the paper presents a compelling approach, it does not extensively discuss potential limitations or scenarios where SemDAC may underperform compared to other codecs. Additionally, the reliance on HuBERT features may limit the model's applicability to languages or dialects for which such pretrained models are not available.
Broader impact
The implications of this work are significant, as it addresses the growing need for efficient speech compression methods that maintain high perceptual quality and intelligibility, especially in low-bitrate scenarios. This could benefit various applications, including voice communication over constrained networks, speech recognition systems, and assistive technologies. The main contribution of this paper is the introduction of SemDAC, a semantic-aware neural audio codec that effectively integrates semantic codebooks into the speech compression process, leading to improved efficiency and recognition accuracy. This work represents a meaningful advancement in the field of audio processing, particularly in the context of neural codecs, by demonstrating the importance of semantic information in enhancing audio quality at lower bitrates.
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