Internalizing ASR with Implicit Chain of Thought for Efficient Speech-to-Speech Conversational LLM
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Machine learning method internalizing ASR chain of thought in a speech-to-speech conversational LLM; a model architecture contribution.
The preprint develops a speech-to-speech language-model method, not a study of research practice.
Speech LLM engineering for ASR chain-of-thought; AI systems research, not metaresearch.
Abstract
Current speech-based LLMs are predominantly trained on extensive ASR and TTS datasets, excelling in tasks related to these domains. However, their ability to handle direct speech-to-speech conversations remains notably constrained. These models often rely on an ASR-to-TTS chain-of-thought pipeline, converting speech into text for processing before generating audio responses, which introduces latency and loses audio features. We propose a method that implicitly internalizes ASR chain of thought into a speech LLM, enhancing its native speech understanding capabilities. Our approach reduces latency and improves the model's native understanding of speech, paving the way for more efficient and natural real-time audio interactions. We also release a large-scale synthetic conversational dataset to facilitate further research.
Stored with the screening record, where it is evidence for the labels above.
The record
- Venue
- arXiv (Cornell University)
- Topic
- Speech and dialogue systems
- Field
- Computer Science
- Canadian institutions
- —
- Funders
- Alliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
- Keywords
- Speech recognitionPsychologyIndirect speechComputer scienceLinguisticsCognitive psychologyNatural language processingPhilosophy
- Has abstract in OpenAlex
- yes