Abstract
Employing Second-Order Circular Suprasegmental Hidden Markov Models to Enhance Speaker Identification Performance in Shouted Talking Environments
Highlights
Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information embedded in speech signals
This paper aims at proposing, implementing and testing new models to enhance text-dependent speaker identification performance in shouted talking environments
The main goal of this work is to further improve speaker identification performance in such talking environments based on a combination of each of: HMM2s, CHMM2s and Suprasegmental Hidden Markov Models (SPHMMs)
Summary
Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information embedded in speech signals. Speaker recognition involves two applications: speaker identification and speaker verification (authentication). Speaker identification is the process of finding the identity of the unknown speaker by comparing his/her voice with voices of registered speakers in the database. Speaker identification can be used in criminal investigations to determine the suspected persons who generated the voice recorded at the scene of the crime. Speaker identification can be used in civil cases or for the media. These cases include calls to radio stations, local or other government authorities, insurance companies, monitoring people by their voices and many other applications
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