AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling
Abstract
Neural speech codecs provide discrete representations for speech language models, but emotional cues are often degraded during quantization. Existing codecs mainly optimize acoustic reconstruction, leaving emotion expressiveness insufficiently modeled at the representation level. We propose an emotion-guided neural speech codec that explicitly preserves emotional information while maintaining semantic fidelity and prosodic naturalness. Our framework combines emotion-semantic guided latent modulation, relation-preserving emotional-semantic distillation, and emotion-weighted semantic alignment to retain emotionally salient cues under compression. Extensive evaluations across speech reconstruction, emotion recognition, and downstream text-to-speech generation demonstrate improved emotion consistency and perceptual quality without sacrificing content accuracy.
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 introduces AffectCodec, a neural speech codec that prioritizes emotion preservation in speech representations, significantly enhancing the emotional expressiveness of synthesized speech while maintaining high levels of semantic fidelity and prosodic naturalness. The innovative integration of emotion-aware optimization strategies marks a substantial advancement in the field of audio processing and speech synthesis.
Methodology
The paper presents a novel approach to neural speech coding by explicitly integrating emotion preservation into the codec's optimization objectives. The methodology is well-structured, comprising three key components: Emotion-Semantic Guided Latent Modulation, Relation-Preserving Emotional-Semantic Distillation, and Emotion-Weighted Semantic Alignment. This multi-faceted approach is innovative as it addresses a significant gap in existing codecs that typically overlook emotional expressiveness during quantization. The use of frozen emotion and semantic models to guide the codec's learning process is particularly noteworthy, enhancing the emotional fidelity of the generated speech.
Results & evaluation
The experiments are extensive and rigorously designed, evaluating the proposed codec across multiple benchmarks, including EMO-SUPERB and LibriSpeech, for both speech emotion recognition and text-to-speech synthesis. The results demonstrate significant improvements in emotional consistency, perceptual quality, and content preservation compared to baseline models. The use of both objective metrics (like WER and PESQ) and subjective evaluations (like UTMOS and MUSHRA) provides a comprehensive assessment of the codec's performance.
Reproducibility
The paper provides detailed implementation information, including training procedures, model architectures, and evaluation metrics. However, while the methodology is well-documented, the actual code repository or supplementary materials for reproduction are not provided, which could hinder reproducibility for other researchers.
Limitations
The paper acknowledges the computational efficiency of the codec but suggests that future work could explore lighter-weight architectures. Additionally, while the emotional preservation is significantly improved, the paper does not address potential challenges in real-world applications, such as variability in emotional expression across different speakers or contexts.
Broader impact
The proposed codec has the potential to enhance applications in conversational AI, voice synthesis, and emotional computing, where preserving emotional nuances is critical. By improving the fidelity of emotional expression in synthesized speech, this work could lead to more engaging and human-like interactions in voice-based systems. This paper introduces AffectCodec, a neural speech codec that prioritizes emotion preservation in speech representations, significantly enhancing the emotional expressiveness of synthesized speech while maintaining high levels of semantic fidelity and prosodic naturalness. The innovative integration of emotion-aware optimization strategies marks a substantial advancement in the field of audio processing and speech synthesis.
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