- The paper introduces a causal speech enhancement method that leverages future semantic prediction through quantized self-supervised learning features.
- It employs feature-wise linear modulation and multi-task learning to combine SSL and spectrogram data, achieving a PESQ of 2.88 on VoiceBank + DEMAND.
- The study demonstrates the potential of integrating discrete semantic tokens and suggests future improvements like inherent causality in SSL and enhanced phase estimation.
Overview of "Causal Speech Enhancement with Predicting Semantics based on Quantized Self-supervised Learning Features"
This paper presents a novel approach to causal speech enhancement (SE) that leverages self-supervised learning (SSL) features combined with future semantic prediction. The proposed method is centered on predicting future phonetic information, extending beyond existing causal SE models which utilize only past context for enhancement. The authors introduce the novel integration of SSL features, augmented by predictive semantics, into the SE workflow, making this paper a pioneering effort in this domain.
Methodology
The core innovation in this paper is the use of quantized SSL features to guide the SE process with causal constraints. The SSL models, typically non-causal, are constrained to utilize only past frames to simulate causality by applying features in a frame-wise manner. This process is refined through the use of feature-wise linear modulation (FiLM), which enhances the fusion of SSL and spectrogram features.
Furthermore, the paper introduces an encoder framework that transforms SSL features into discrete semantic tokens via vector quantization. These tokens are then used for multi-task learning where the SE model not only enhances the speech signal but also predicts future semantic tokens through a LLMing approach. This joint task is hypothesized to assist in anticipating phonetic continuity, thereby enhancing model performance.
Results
The experimental evaluation was conducted using the VoiceBank + DEMAND dataset. The proposed model achieved a perceptual evaluation of speech quality (PESQ) score of 2.88, underscoring a notable improvement over several conventional SE models. Notably, the semantic prediction component of the model increases the PESQ by 0.05, indicative of its contribution towards effective causal SE.
Implications and Future Directions
This paper contributes a multi-task learning approach in causal SE, showcasing how predictive semantics can be effectively leveraged even within causal constraints. This approach can inform the design of future SE models by highlighting the utility of semantic predictions in improving speech signals under noisy conditions. The utilization of quantized SSL features offers a scalable framework, as large amounts of unlabeled data can be effectively harnessed in self-supervised manners.
For future exploration, the paper suggests improvements such as training SSL models that inherently support causality instead of adapting pre-trained models designed for batch processing. Furthermore, enhancing the model to include estimations of appropriate phase information could push the performance closer to state-of-the-art levels. This paper opens avenues for exploring discrete representation frameworks such as neural audio codecs for predictions in SE tasks, which could further refine real-time SE applications.
In conclusion, this paper presents an innovative step in integrating semantic prediction into causal SE systems, thereby providing a methodological foundation for subsequent research in enhancing real-time speech processing capabilities.