Training Speech Recognition Models to Follow Instructions

Abstract

Conventional end-to-end Automatic Speech Recognition (ASR) models primarily focus on exact transcription tasks, lacking flexibility for nuanced user interactions. In this paper, we train a speech recognition model to follow a diverse set of free-form text instructions for a multitude of speech recognition tasks -- ranging from simple transcript manipulation to summarization. We emphasize that even without pre-trained LLMs or speech modules, a Listen-Attend-Spell model trained from scratch on Librispeech understands and executes instructions with high fidelity. This preliminary findings highlight the potential of instruction-following training to advance speech foundation models.