The Application of Time-Frequency Masking To Improve Intelligibility of Dysarthric Speech in Background Noise

Background noise reduces speech intelligibility. Time-frequency (T-F) masking is an established signal processing technique that improves intelligibility of neurotypical speech in background noise. Here, we investigated a novel application of T-F masking, assessing its potential to improve intelligibility of neurologically degraded speech in background noise.

Listener participants (N = 422) completed an intelligibility task either in the laboratory or online, listening to and transcribing audio recordings of neurotypical (control) and neurologically degraded (dysarthria) speech under three different processing types: speech in quiet (quiet), speech mixed with cafeteria noise (noise), and speech mixed with cafeteria noise and then subsequently processed by an ideal quantized mask (IQM) to remove the noise.

We observed significant reductions in intelligibility of dysarthric speech, even at highly favorable signal-to-noise ratios (+11 to +23 dB) that did not impact neurotypical speech. We also observed significant intelligibility improvements from speech in noise to IQM-processed speech for both control and dysarthric speech across a wide range of noise levels. Furthermore, the overall benefit of IQM processing for dysarthric speech was comparable with that of the control speech in background noise, as was the intelligibility data collected in the laboratory versus online.

This study demonstrates proof of concept, validating the application of T-F masks to a neurologically degraded speech signal. Given that intelligibility challenges greatly impact communication, and thus the lives of people with dysarthria and their communication partners, the development of clinical tools to enhance intelligibility in this clinical population is critical.