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The past ten years have witnessed the rapid development of text-based intentdetection, whose benchmark performances have already been taken to a remarkablelevel by deep learning techniques. However, automatic speech recognition (ASR)errors are inevitable in real-world applications due to the environment noise,unique speech patterns and etc, leading to sharp performance drop instate-of-the-art text-based intent detection models. Essentially, thisphenomenon is caused by the semantic drift brought by ASR errors and mostexisting works tend to focus on designing new model structures to reduce itsimpact, which is at the expense of versatility and flexibility. Different fromprevious one-piece model, in this paper, we propose a novel and agile frameworkcalled CR-ID for ASR error robust intent detection with two plug-and-playmodules, namely semantic drift calibration module (SDCM) and phonemicrefinement module (PRM), which are both model-agnostic and thus could be easilyintegrated to any existing intent detection models without modifying theirstructures. Experimental results on SNIPS dataset show that, our proposed CR-IDframework achieves competitive performance and outperform all the baselinemethods on ASR outputs, which verifies that CR-ID can effectively alleviate thesemantic drift caused by ASR errors.

Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection