Reverberation matching for speaker recognition

Speech recorded by a distant microphone in a room may be subject to reverberation. Performance of a speaker verification system may degrade significantly for reverberant speech, with severe consequences in a wide range of real applications. This paper presents a comprehensive study of the effect of reverberation on speaker verification, and investigates approaches to reduce the effect of reverberation: training target models with reverberant speech signals and using acoustically matched models for the reverberant speech under test, score normalization methods to improve the reverberation robustness, and also reverberation classification via the background model scores. Experimental investigation is performed, using simulated and measured room impulse responses, NIST-based speech database, and AGMM based speaker verification system, showing significant improvement in performance.