Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
| Autor: | Mitra, Vikramjit, Huang, Zifang, Lea, Colin, Tooley, Lauren, Wu, Sarah, Botten, Darren, Palekar, Ashwini, Thelapurath, Shrinath, Georgiou, Panayiotis, Kajarekar, Sachin, Bigham, Jefferey |
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| Rok vydání: | 2021 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Dysfluencies and variations in speech pronunciation can severely degrade speech recognition performance, and for many individuals with moderate-to-severe speech disorders, voice operated systems do not work. Current speech recognition systems are trained primarily with data from fluent speakers and as a consequence do not generalize well to speech with dysfluencies such as sound or word repetitions, sound prolongations, or audible blocks. The focus of this work is on quantitative analysis of a consumer speech recognition system on individuals who stutter and production-oriented approaches for improving performance for common voice assistant tasks (i.e., "what is the weather?"). At baseline, this system introduces a significant number of insertion and substitution errors resulting in intended speech Word Error Rates (isWER) that are 13.64\% worse (absolute) for individuals with fluency disorders. We show that by simply tuning the decoding parameters in an existing hybrid speech recognition system one can improve isWER by 24\% (relative) for individuals with fluency disorders. Tuning these parameters translates to 3.6\% better domain recognition and 1.7\% better intent recognition relative to the default setup for the 18 study participants across all stuttering severities. Comment: 5 pages, 1 page reference, 2 figures |
| Databáze: | arXiv |
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