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Abstract

Intelligent Personal Assistant (IPA) devices such as Google Home and Amazon Echo have become commodity hardware and are well-known in the public domain. Leveraging these devices as speech-based interfaces to bespoke conversation agent (CA) systems in vocabulary-specific domains exposes their underlying Automatic Speech Recognition (ASR) transcription error rates, which are usually hidden behind a probability matching of utterance to intent (slot filling). We present an evaluation of the two aforementioned IPA’s isolated word and phrasal recognition rates together with an improvement scheme associated with a Contextual Multiple Classification Ripple Down Rules (C-MCRDR) CA knowledge-base system (KBS). When measuring isolated-word word error rates (WER) for a human speaker, Google Home achieved an average WER of 0.082 compared to 0.276 for Amazon Echo. Computer-generated utterances unsurprisingly had much poorer recognition rates, with WER for Google Home and Amazon Echo of 0.155 and 0.502 respectively. For phrasal tests, Google Home had an average WER of 0.066 in comparison to the Amazon Echo WER of 0.242 when processing human-sourced sentences. We applied a rule-based transcription error-correcting scheme for isolated words and achieved correct recognition rates of 100% for the Google Home in five of the isolated word data sets, and across all isolated words datasets we improved the initial average WER of 0.082 to 0.0153, a significant decrease of 81.34%.

Item Type:	Conference Publication
Authors/Creators:	Herbert, DP and Kang, B
Keywords:	MCRDR, intelligent personal assistant, knowledge-base systems, conversational agent
Journal or Publication Title:	Lecture Notes in Computer Science
Publisher:	Springer Nature
DOI / ID Number:	https://doi.org/10.1007/978-3-030-30639-7_11
Copyright Information:	Copyright 2019 Springer Nature
Related URLs:	UTAS Profile: Herbert, D Google Scholar: Kang, B UTAS Profile: Kang, B
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