Current behind-the-ear hearing aids (HA) allow to perform spatial filtering to enhance localized sound sources; however, they often lack processing strategies that are tailored to spoken language. Hence, without a feedback about speech quality achieved by the system, spatial filtering potentially remains unused, in case of a conservative enhancement strategy, or can even be detrimental to the speech intelligibility of the output signal. In this paper we apply phoneme posteriorgrams obtained from HA signals processed with deep neural networks to measure the quality of speech representations in spatial scenes. Inverse entropy of phoneme probabilities is proposed as a measure that allows to evaluate if current hearing aid parameters are optimal for the given acoustic condition. We investigate how varying noise levels and wrong estimates of the to-be-enhanced direction affect this measure in anechoic and reverberant conditions and show our measure to provide a high reliability when varying each parameter. Experiments show that entropy as a function of the beam angle has a distinct minimum at the speaker’s true position and its immediate vicinity. Thus, it can be used to determine the beam angle which optimizes the speech representation. Further, variations of the SNR cause a consistent offset of the entropy.
Cite as: Spille, C., Kayser, H., Hermansky, H., Meyer, B.T. (2016) Assessing Speech Quality in Speech-Aware Hearing Aids Based on Phoneme Posteriorgrams. Proc. Interspeech 2016, 1755-1759, doi: 10.21437/Interspeech.2016-1318
@inproceedings{spille16_interspeech,
author={Constantin Spille and Hendrik Kayser and Hynek Hermansky and Bernd T. Meyer},
title={{Assessing Speech Quality in Speech-Aware Hearing Aids Based on Phoneme Posteriorgrams}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={1755--1759},
doi={10.21437/Interspeech.2016-1318}
}