2019 · 14 citations · 14 references
ComputingSpeech-language PathologyAcousticsFeature EngineeringPhoneticsAcoustic PhoneticsDigital Signal ProcessingFeature ExtractionPattern RecognitionSpeaker IdentificationSpeech RecognitionAccuracyAcoustic Feature ExtractionAudio Feature ExtractionBiometric Feature ExtractionCepstral CoefficientsFeature Engineering For Audio SignalsNoise Robust Speech AnalysisSpeaker Recognition
The Mel-frequency Cepstral Coefficients (MFCC) method is the most popular method and has good performance as a feature extraction to date. But based on the simulation results, it is known that the speaker recognition system that uses MFCC as a feature extraction has low accuracy when applied to voice containing noise. So in this study, we propose the DWT-MFCC method to overcome this problem. The simulation results show that the DWT-MFCC method has higher accuracy compare with conventinal MFCC method when applied as feature extraction in the speaker recognition system with SNR from 15 to 40 dB.
14
An Algorithm for Vector Quantizer Design
Y. Linde, A. Buzo, Robert M. Gray · IEEE Transactions on Communications · 1980 · 7.2K citations
Librispeech: An ASR corpus based on public domain audio books
Vassil Panayotov, Guoguo Chen, Daniel Povey et al. · 2015 · 6K citations
Computing, Computer Science, English +17