arXiv:2602.15537v2 Announce Type: replace Abstract: Pure speech language models aim to learn language directly from raw audio without textual resources. A key challenge is that discrete tokens from self-supervised speech encoders result in excessively long sequences, motivating recent work on syllable-like units. However, methods like Sylber and SyllableLM rely on intricate multi-stage training pipelines. We propose ZeroSyl, a simple training-free method to extract syllable boundaries and embeddings directly from a frozen WavLM model. Using L2 norms of features in WavLM's intermediate layers,
Ongoing research in spoken language modeling is pushing for more efficient and robust methods that learn directly from audio, eliminating reliance on extensive textual data.
This work represents a key step towards more versatile and efficient AI speech understanding, particularly beneficial for low-resource languages or contexts where textual data is scarce.
The development of 'ZeroSyl' suggests a simplified approach to derive crucial linguistic units (syllables) from raw audio, potentially accelerating the development of pure speech language models by removing complex multi-stage training pipelines.
- · AI researchers in speech processing
- · Developers of AI in low-resource language regions
- · Startups building audio-first AI applications
- · Developers of complex multi-stage speech preprocessing pipelines
Easier and faster development of speech-based AI models.
Improved accuracy and efficiency of AI applications in multilingual and diverse audio environments.
Reduction in data dependency for speech AI, democratizing access and development.
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