2004 · 14 citations · 3 references
Health ProfessionsAudiologyNeuroscienceAcousticsHearing SciencePsychoacousticsDigital Signal ProcessingNoise ReductionSpeech RecognitionAcoustic Feature ExtractionAudio Feature ExtractionFeature Engineering For Audio SignalsNoise Robust Speech AnalysisPitch DeterminationRobust Feature RepresentationRobust Speech RecognitionTime-frequency Feature Extraction
In this paper, we propose a novel feature extraction method based on an auditory nervous system for robust automatic speech recognition (ASR). In the proposed method, a pitchsynchronous mechanism is embedded in ZCPA (ZeroCrossings Peak-Amplitudes), which has previously been shown to outperform the conventional features in the presence of noise. A noise-robust non-delayed pitch determination algorithm (PDA) is also developed. In the experiment, the proposed pitch-synchronous ZCPA (PS-ZCPA) was proved more robust than the original ZCPA method. Moreover, a simple noise subtraction (NS) method is also integrated in the proposed method and the performance was evaluated using the Aurora-2J database. The experimental results showed the superiority of the proposed PS-ZCPA method with NS over the PS-ZCPA method without NS.
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