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A. Vizinho, P. Green, M. P. Cooke, and L. Josifovski, “Missing data theory, spectral subtraction and signal-to- noise estimation for robust ASR: An integrated study [C],” Proceedings of Sixth European Conference on Speech Communication and Technology, Eurospeech, Budapest, pp. 2407–2410, 1999.

has been cited by the following article:

• TITLE: Neural Network Based Missing Feature Method For Text-Independent Speaker Identification

  AUTHORS: Ying WANG, Wei LU

  KEYWORDS: Speaker Identification, Missing Feature Reconstruction, Mask Estimation, Neural Network

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.3 No.1, January 20, 2010

  ABSTRACT: The first step of missing feature methods in text-independent speaker identification is to identify highly corrupted spectrographic representation of speech as missing feature. Most mask estimation techniques rely on explicit estimation of the characteristics of the corrupting noise and usually fail to work with inaccurate estimation of noise. We present a mask estimation technique that uses neural networks to determine the reliability of spectrographic elements. Without any prior knowledge of the noise or prior probability of speech, this method exploits only the characteristics of the speech signal. Experiments were performed on speech corrupted by stationary F16 noise and non-stationary Babble noise from 5dB to 20 dB separately, using cluster based reconstruction missing feature method. The result performs better recognition accuracy than conventional spectral subtraction mask estimation methods.