Ambience Inhaling: Speech Noise Inhaler in Mobile Robots using Deep Learning

Abstract

Audio based, machine learning human-computer interface with speech recognition systems performs sensibly well with the human voice under clean ambience, but become frail in applied technological implementation involving real-life interface. In mobile robotic systems, the speech machines are normally retrained with new changing acoustic ambience conditions are to be met. To inhale, classify, and track the real-world ambience noise with the new changing acoustic condition, we introduce an Ambience Inhaling (AI) framework in this article. This framework of an AI is to seek out complete noise information from speech data, in contrast with noise-nature discovery. Our proposed framework uses a deep convolutional neural network (CNN) based learning for classification with speech spectrogram patch segments, including a hybrid Harold Hotelling's T-square algorithm with Bayesian statistics for segmentation analysis. We use a symposium presentation-ambience as a test platform. In the symposium presentation-ambience, noise modeling is done with n-gram language having the parameter of n = 2. The impulsive or short-term noise which is superimposed with long-term noise caused degradation in classification. This degradation caused the classification errors. The provision of decision was made. The Gaussian mixture model and hidden Markova model are used with noise-only and noisy speech respectively. Time and frequency pooling are used with spectrogram also. The classification scores of 62.26%, 65.89%, and 69.12% are achieved with 5, 10 and 15 CNN filters respectively. As a significance, an AI is efficient and innovative.