Automatic Speaker Recognition System in Adverse Conditions — Implication of Noise and Reverberation on System Performance

Abstract

Speaker recognition has been developed and evolved over the past few decades into a supposedly mature technique. Existing methods typically utilize robust features extracted from clean speech. In real-world applications, especially security and forensics related ones, reliability of recognition becomes crucial, meanwhile limited speech samples and adverse acoustic conditions, most notably noise and reverberation, impose further complications. This paper is presented from a study into the behavior of typical speaker recognition systems in adverse retrieval phases. Following a brief review, a speaker recognition system was implemented using the MSR Identity Toolbox by Microsoft. Validation tests were carried out with clean speech and the speech contaminated by noise and/or reverberation of varying degrees. The image source method was adopted to take into account real acoustic conditions in the spaces. Statistical relationships between recognition accuracy and signal to noise ratios or reverberation times have therefore been established. Results show noise and reverberation can, to different extents, degrade the performance of recognition. Both reverberation time and direct to reverberation ratio can affect recognition accuracy. The findings may be used to estimate the accuracy of speaker recognition and further determine the likelihood a particular speaker.