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Q-SPT: Learnable Query-Based Compression for Low-Frame-Rate Speech Tokenization
Neural speech codecs increasingly serve as tokenizers for speech language models (SLMs). Lowering the frame rate reduces the computational and memory costs of SLMs, but makes it difficult to preserve both linguistic information and acoustic detail. Existing approaches rely on rule-based compression:
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