Classification of speech signal using hybrid deep reinforcement learning

Abstract

Speech is rapidly being employed not just as a means of newlinecommunication but also in a broad variety of automated applications. This is newlinebecause speech is the biometric trait that is capable of the most expressive newlineexpression. This is because human voice is capable of being translated into a newlinewide variety of languages. Speech processing is responsible for a wide variety newlineof phenomena, including the identification of identities, the recognition of newlineemotions and attention, as well as other phenomena of a similar nature. newlineThere is a great deal of available models that can be utilized for the newlinepurpose of developing automated speaker or voice recognition systems. This newlineprogram can either be digital or analog, depending on the preference. It is newlinefeasible to verify the presence of individual person s identity. Accuracy of newlineclassification is of the utmost value because there are several concerns that newlinecould contribute to a reduction in the accuracy of a speaker identification newlinesystem. newlineThis research intends to select some persons who are having a newlineconversation as part of a larger group that also includes several other newlineparticipants to highlight their perspectives. When numerous speakers are newlineplaced near one another, it is possible for them to generate interference with newlineone another and enhance the total level of background noise. This can happen newlineif the speakers are not placed far enough apart. The level of speaker newlineparticipation can be continuous or fragmented throughout numerous speech newlinesegments, which adds to the issues that are already posed by the speech and newlineenvironment levels. newline