arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering Activations), a lightweight adaptation method that learns layer-wise steering vectors. Unlike commonly used steering approaches that rely on contrastive activation differences, SALSA directly optimizes steering vectors using a supervised objective. Across children's speech, multilingual speech, and Mandarin-English code-switching benchmarks, SALSA substantially improves performance over zero-shot
The proliferation of Large Language Models (LLMs) and their deployment in diverse speech-aware applications creates an urgent need for robust adaptation methods to address out-of-domain performance issues.
Improving the generalization of speech-aware LLMs to varied linguistic and demographic contexts is crucial for their reliable and equitable deployment, expanding their utility across a wider user base.
This research provides a more effective and lightweight method for adapting LLMs to various speech domains, potentially accelerating the development of more inclusive and robust voice-enabled AI systems.
- · AI developers
- · Speech technology companies
- · Multilingual users
- · Children's educational technology
- · Companies relying on less flexible or less accurate speech adaptation methods
Improved performance of speech-aware LLMs in diverse scenarios, enhancing their practical utility.
Accelerated adoption of voice interfaces and AI assistants in previously underserved or challenging linguistic environments.
Enhanced accessibility and inclusivity of AI technologies for a broader global population, reducing digital divides based on language or speech characteristics.
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Read at arXiv cs.CL