A Deconvolutive Neural Network for Speech Classification With Applications to Home Service Robot

Reverberation deteriorates the quality and intelligibility of speech, leading to the poor performance of classification systems. Room reverberation parameters depend on the location of the speaker and the microphone and the room geometry. For mobile robots, the reverberation is constantly changing due to the relative movement of the speaker and the robot. This can affect the spectral properties of the signal and therefore, the classification accuracy. The contribution of this paper is a new network architecture, which uses neural network constant modulus algorithm (NNCMA) based equalizer followed by a multi-layer preceptron (MLP) classifier. NNCMA is an MLP which is trained with a cost function similar to constant modulus algorithm (CMA). With this two-stage structure, the classifier does not have to consider the time-varying nature of the reverberation. The proposed algorithm is applied to speech samples collected by the home service robot WEVER-R2 for speaker classification in a typical home or office environment. We use them for gender classification application. The proposed neural network was found to have 83.73% of classification accuracy for age classification and 88.91% of classification accuracy for gender classification, while the standard MLP had a classification accuracy of 71.43% and 72.29%, respectively.