Analysis and Tuning of a Voice Assistant System for Dysfluent Speech
Abstract
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.
- Publication:
-
arXiv e-prints
- Pub Date:
- June 2021
- DOI:
- 10.48550/arXiv.2106.11759
- arXiv:
- arXiv:2106.11759
- Bibcode:
- 2021arXiv210611759M
- Keywords:
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- Electrical Engineering and Systems Science - Audio and Speech Processing;
- Computer Science - Artificial Intelligence;
- Computer Science - Computation and Language;
- Computer Science - Computer Vision and Pattern Recognition;
- Computer Science - Machine Learning;
- Computer Science - Sound
- E-Print:
- 5 pages, 1 page reference, 2 figures