Elhard James Kumalija
ABSTRACT
There are a growing number of installations of network speakers in public space like train stations, schools, and hospitals. These speakers are used for announcements and playing background music. Network performance can affect the quality of announcement speech heard from the network speaker. In this study, a deep neural network method is proposed for live monitoring of the quality of speaker output as perceived by public space occupants. Single end method for speech quality assessment was proposed because of the nature of the application, there is no reference speech to use for assessment. The network node (end point) of the network speaker usually has low memory and computing resource. Therefore, compact deep neural network architecture and post-training quantization method were examined as deep neural network compression techniques for memory saving and compute acceleration. Using PESQ which is an end-to-end assessment method as the baseline for comparing the proposed method and ITU-T P.563 which are single-end methods, the estimated mean opinion score Pearson correlation coefficient was 0.710 and 0.40 for proposed method and P.563 respectively. The mean squared error for proposed method and P.563 was 0.154 and 0.319, respectively. The proposed method performed better than P.563 ITU-T recommended method.
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