Unlocking Speech-Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning
arXiv:2607.02214v1 Announce Type: new Abstract: Instruction tuning for speech language models (SLMs) is substantially more challenging than for text-based large language models (LLMs), as it requires learning a new modality and a wide range of speech-specific instructions in addition to those supported by text LLMs. Existing SLM training approaches largely replicate the text LLM training paradigm by synthesizing large-scale speech pre-training and instruction-tuning datasets. However, this strategy is difficult to scale, since speech sequences are significantly longer than text sequences. In t
The paper unveils a novel approach to training instruction-following Speech Language Models (SLMs) without the need for extensive instruction tuning, addressing current scalability challenges.
This research provides a more efficient pathway for developing advanced SLMs, potentially accelerating the deployment of sophisticated voice-controlled AI agents and ubiquitous AI assistants.
Current speech language model training reliance on large-scale, difficult-to-scale instruction-tuning datasets is challenged by a new methodology, potentially simplifying future SLM development.
- · AI research labs
- · Speech technology companies
- · Developers of AI assistants
- · Edge AI hardware manufacturers
- · Companies heavily invested in traditional SLM instruction tuning
- · Data annotation services focused on speech instruction tuning
More sophisticated and efficient speech language models become feasible due to reduced training complexity.
The development of highly capable AI agents and interfaces that rely heavily on natural speech interaction could accelerate significantly.
Wider adoption of speech-driven AI in various sectors, from customer service to defense, leading to a new wave of human-computer interaction paradigms.
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