An Algorithm for Detection of Breath Sounds in Spontaneous Speech with Application to Speaker Recognition

Abstract

Automatic detection and demarcation of non-speech sounds in speech is critical for developing sophisticated human-machine interaction systems. The main objective of this study is to develop acoustic features capturing the production differences between speech and breath sounds in terms of both, excitation source and vocal tract system based characteristics. Using these features, a rule-based algorithm is proposed for automatic detection of breath sounds in spontaneous speech. The proposed algorithm outperforms the previous methods for detection of breath sounds in spontaneous speech. Further, the importance of breath detection for speaker recognition is analyzed by considering an i-vector-based speaker recognition system. Experimental results show that the detection of breath sounds, prior to i-vector extraction, is essential to nullify the effect of breath sounds occurring in test samples on speaker recognition, which otherwise will degrade the performance of i-vector-based speaker recognition systems.