Detecting Signal Corruptions in Voice Recordings For Speech Therapy

Abstract

In this article we design an experimental setup to detect disturbances in voice recordings, such as additive noise, clipping, infrasound and random muting. The datasets are generated by introducing degradations into clean recordings. We test five different classification algorithms in both single- and multi-label settings: kernel substitution based support vector machine, convolutional neural network, long short-term memory (LSTM), and a hidden Markov model using either Gaussian mixture models or generative models in its state distribution. The LSTM achieved good results in both tests, most notably in the multi-label case where the average balanced accuracy was 82.7% on one dataset.