An efficient text-independent speaker verification for short utterance data from Mobile devices

Abstract

Speaker verification is the process used to recognize a speaker from his/her voice characteristics by extracting the features. Speaker verification with text-independent data is a process of verifying the speaker identity without limitation in the speech content. In the speaker verification process, long utterances are normally used but it contains lot of silences leading to complexity and more disruptions. So, we are performing speaker verification method based on short utterance data. The main objective of the research work is to extract, characterize, and recognize the information about speaker identity. Our proposed work contains four stages: 1) utterance partitioning, 2) feature extraction, 3) feature selection, and 4) classification. In our proposed model, an utterance partitioning approach is used to shorten the full-length speech into numerous short-length utterances before the pre-processing stage. In the feature extraction phase, noise removal is carried out with pre-emphasis filter in the pre-processing step. The Mel Advanced Hilbert-Huang Cepstral Coefficients (MAHCC) technique is used for extracting the features from the given input speech signal. Furthermore, the feature selection process is done with the help of a Crow Search Algorithm (CSA) by ranking the given feature set to obtain optimal features for classification. In the classification stage, the Deep Hidden Markov Model (DHMM) method is introduced to classify the features for speaker verification with discriminative pre-training process. Thus, the proposed approach provides an accurate classification and the implementation results show that the performance of the proposed method is better than the existing methods.