Nonverbal Sound Detection for Disordered Speech

TL;DR

This paper presents a nonverbal sound detection system using mouth sounds to improve voice assistant accessibility for individuals with speech disorders, demonstrating high accuracy and effective personalization.

Contribution

It introduces a novel sound event detection approach using nonverbal mouth sounds, with dataset design, model considerations, and personalization strategies for disordered speech.

Findings

Achieves 88.6% precision and 88.4% recall on internal dataset

0.31 false positives per hour on speech aggressors

84.5% success rate in personalized model performance

Abstract

Voice assistants have become an essential tool for people with various disabilities because they enable complex phone- or tablet-based interactions without the need for fine-grained motor control, such as with touchscreens. However, these systems are not tuned for the unique characteristics of individuals with speech disorders, including many of those who have a motor-speech disorder, are deaf or hard of hearing, have a severe stutter, or are minimally verbal. We introduce an alternative voice-based input system which relies on sound event detection using fifteen nonverbal mouth sounds like "pop," "click," or "eh." This system was designed to work regardless of ones' speech abilities and allows full access to existing technology. In this paper, we describe the design of a dataset, model considerations for real-world deployment, and efforts towards model personalization. Our fully-supervised model achieves segment-level precision and recall of 88.6% and 88.4% on an internal dataset of 710 adults, while achieving 0.31 false positives per hour on aggressors such as speech. Five-shot personalization enables satisfactory performance in 84.5% of cases where the generic model fails.