Author(s)

Hisako Orimoto¹, Akira Ikuta¹, Kouji Hasegawa²

¹Department of Management Information Systems, Prefectural University of Hiroshima, Hiroshima, Japan.
²Western Region Industrial Research Center, Hiroshima Prefectural Technology Research Institute, Kure, Japan.

In order to apply speech recognition systems to actual circumstances such as inspection and maintenance operations in industrial factories to recording and reporting routines at construction sites, etc. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. In this study, a signal detection method to remove the noise for actual speech signals is proposed by using Bayesian estimation with the aid of bone-conducted speech. 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 is theoretically derived. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. The effectiveness of the proposed method is experimentally confirmed by applying it to air- and bone-conducted speeches measured in real environment under the existence of surrounding background noise.

Speech Signal Detection, Bayesian Estimation, Air- and Bone-Conducted Speeches, Surrounding Noise

Orimoto, H. , Ikuta, A. and Hasegawa, K. (2021) Speech Signal Detection Based on Bayesian Estimation by Observing Air-Conducted Speech under Existence of Surrounding Noise with the Aid of Bone-Conducted Speech. Intelligent Information Management, 13, 199-213. doi: 10.4236/iim.2021.134011.