![]() įurthermore, in our previous study, a signal processing method to remove the noise for actual speech signals was proposed by jointly using the measured data of bone-and air-conducted speeches. From the above viewpoint, 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. The actual noises show complex fluctuation forms with non-Gaussian and non-white properties. In such a noise reduction task for speech signals based on a single microphone, many algorithms applying Kalman filter have been proposed up to now by assuming Gaussian white noise. Therefore, the former based on a single microphone is more advantageous than the latter. Since the latter requires prior information on the number of noise sources, and the number of microphones needed is larger than that of the noise sources in the case of multi-noise sources, this category demands large scale systems. One is based on a single microphone and the other uses a microphone array. Previously reported methods for noise reduction in speech recognition can be classified into two categories. For speech recognition in such actual circumstances, some countermeasure methods for surrounding noises are indispensable. For example, these systems are applied to inspection and maintenance operations in industrial factories and to recording and reporting routines at construction sites, etc. Many kinds of speech recognition systems have been developed according to the progress of digital information technique. ![]() 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. In the proposed speech detection method, bone-conducted speech is utilized in order to obtain precise estimation for speech signals. 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 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. where hand-writing is difficult, some countermeasure methods for surrounding noise are indispensable. It does not store any personal data.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. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is used to store the user consent for the cookies in the category "Other. The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly.
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