Overview of speech enhancement techniques for automatic speaker recognition

By Javier Ortega-Garcia, Joaquin Gonzalez-Rodriguez. 1996

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. 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 on the desired signal, giving rise to techniques involved in the enhancement of noisy speech. A comparative performance analysis of single-channel (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 HMM-based speaker recognizer, is presented.

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Citations in the corpus

SEGAN: Speech Enhancement Generative Adversarial Network (2017, methodology): “Classic speech enhancement methods are spectral subtraction [6], Wiener ﬁltering [7], statistical model-based meth-ods [8], and subspace algorithms [9, 10].”
Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks (2017, background): “2784878 [3], [4], speech coding [5], [6], hearing aids [7]–[9], and cochlear implants [10]–[12].”