arXiv:2603.05299v2 Announce Type: replace-cross Abstract: Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the entanglement of semantic and acoustic information. Most existing speech language models rely on text supervision, hierarchical token streams, or complex hybrid architectures, departing from the single-stream generative pretraining paradigm that has proven effective in text. In this work, we introduce WavSLM, a speech language model trained by quantizing and distil
The proliferation of large language models (LLMs) in text necessitates extending their success paradigms to other modalities, making advancements in speech language models a logical next step.
Developing single-stream, autoregressive speech language models like WavSLM could unlock more scalable and coherent speech generation and understanding, paralleling the transformative impact of LLMs on text.
This research suggests a potential shift towards more efficient and less complex architectures for speech processing, moving away from multi-modal or hierarchical approaches.
- · AI researchers
- · Speech technology developers
- · Voice assistant companies
- · Language model providers
- · Companies reliant on complex, multi-modal speech architectures
WavSLM directly improves the efficiency and scalability of speech language modeling.
This could lead to more natural and sophisticated human-computer interaction through voice interfaces.
Advances in speech AI may eventually blur the lines between human and synthetic voice, impacting industries like entertainment and customer service, while raising new questions of authenticity.
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Read at arXiv cs.CL