Abstract
Speech recognition systems have been applied to inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult. In these actual circumstances, some countermeasure methods for surrounding noise are indispensable. In this study, a new method to remove the noise for actual speech signal was proposed by using Bayesian estimation with the aid of bone-conducted speech and fuzzy theory. More specifically, by introducing Bayes’ theorem based on the observation of air-conducted speech contaminated by surrounding background noise, a new type of algorithm for noise removal was theoretically derived. In the proposed noise suppression method, bone-conducted speech signal with the reduced high-frequency components was regarded as fuzzy observation data, and a stochastic model for the bone-conducted speech was derived by applying the probability measure of fuzzy events. The proposed method was applied to speech signals measured in real environment with low SNR, and better results were obtained than an algorithm based on observation of only air-conducted speech.
Highlights
Speech recognition systems have been applied to various fields, for example, to inspection and maintenance operations in industrial factories and at construction sites, etc. where hand-writing is difficult
In the proposed noise suppression method, bone-conducted speech signal with the reduced high-frequency components was regarded as fuzzy observation data, and a stochastic model for the bone-conducted speech was derived by applying the probability measure of fuzzy events
The proposed method was applied to speech signals measured in real environment with low SNR, and better results were obtained than an algorithm based on observation of only air-conducted speech
Summary
In our previously reported study, a noise suppression algorithm for the actual speech signals without requirement of the assumption of Gaussian white noise has been proposed [11]. Signal processing methods to remove the noise for actual speech signals have been proposed by jointly using the measured data of bone- and air-conducted speech signals [12] [13]. A new noise suppression method for speech signals is proposed by using Bayes theorem after employing a posterior distribution based on the air-conducted speech observation contaminated by surrounding noise. The algorithm proposed in this study is applied to signals measured in real environment under existence of noises. The effectiveness of the proposed method is confirmed by applying it to boneand air-conducted speech measured in a real environment under the existence of surrounding noise
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