Broad phonetic feature classifier for real-time hearing aid processing

Current Hearing Aid (HA) devices compensate for hearing loss by amplifying sounds based on instantaneous energy estimates in specific frequency bands. The dynamics of speech are incorporated using attack and release times—a crude approximation of the rich temporal dynamics in natural speech. Recent advances in silicon enables one to consider more complex sound processing approaches with minimal impact on the HA battery life. We envisage a broad phonetic feature classifier, followed by amplification strategies optimized for specific temporal dynamics across each segment of natural speech. In this work we present a broad phonetic classifier for ‘vocalic’, ‘non-vocalic’ and ‘mixed’ speech segments. The classifier is based on (i) Interpretable models- unsupervised learning methods such as Gaussian Mixture Models followed by supervised learning based Radial Basis Function networks; and (ii) Deep Neural Networks with supervised learning. The frontend feature extractor consists of MFCC, delta MFCC features, spectral flatness measure and segmental energy. The training and test sets are generated using speech files and the corresponding phone labels from the TIMIT dataset. We present our findings with respect to the classifier's performance and deployability for real time HA processing.