Automatic Speaker Identification through Robust Time Domain Features and Hierarchical Classification Approach

Abstract

Speaker Identification systems have become area of intense research in recent years due to its wide range of applications in voice matching, biometric identification, mobile access security, health care management and transportation. The major challenge in speaker identification is how to provide robust and computationally efficient features that accurately captures speaker's unique identity for higher generalized identification. This paper presents hierarchical classification approach for speaker identification using robust time domain features. In hierarchical classification approach, the first level classifier identifies the gender voice (i.e. male or female voice). In addition, the second level classifier identifies the specific speaker voice. The experiments were conducted using the speech data collected from ten subjects including male and female to ensure the generalized model evaluations. Several time domain features were extracted from the collected dataset that were proven highly discriminative for gender identification and specific speaker identification. The proposed method helps to reduce computational time with increase speaker recognition rate. The experimental results obtained highest accuracy of 96.9% using random forest classifier for gender identification. Moreover, for specific speaker identification the highest accuracy of 78% was observed in male speaker identification and 88.7% accuracy was obtained in female speaker identification using random forest classifier. The encouraging results of our experiments show the impact of hierarchical speaker identification using carefully selected time domain features.