Real world conditions differ from ideal or laboratory conditions, causing mismatch between training and testing phases, and consequently, inducing performance degradation in automatic speaker recognition systems [1]. Many strategies have been adopted to cope with acoustical degradation; in some applications of speaker identification systems a clean sample of speech, prior to the recognition stage, is needed. This has justified the use of procedures that may reduce the impact of acoustical noise

more »

... on the desired signal, giving rise to techniques involved in the enhancement of noisy speech [2, 9] . In this paper, a comparative performance analysis of singlechannel (based in classical spectral subtraction and some derived alternatives), dual-channel (based in adaptive noise cancelling) and multi-channel (using microphone arrays) speech enhancement techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMMbased speaker recognizer, is presented.