Feature extraction combining spectral noise reduction and cepstral histogram equalization for robust ASR
نویسندگان
چکیده
This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate the remaining non-linear effects associated to channel distortion and additive noise. The estimation of the noise spectrum for the spectral subtraction method relies on a new algorithm for speech / non-speech detection (SND) based on order statistics. This SND classification is also used for dropping long speech pauses. Results on Aurora 2 and Aurora 3 are reported.
منابع مشابه
Feature Extraction Combining Spe and Cepstral Histogram Equaliz
This work is mainly focused on showing experimental results using a combination of two methods for noise compensation which are shown to be complementary: classical spectral subtraction algorithm and histogram equalization. While spectral subtraction is focused on the reduction of the additive noise in the spectral domain, histogram equalization is applied in the cepstral domain to compensate t...
متن کاملNormalization of spectro-temporal Gabor filter bank features for improved robust automatic speech recognition systems
Physiologically motivated feature extraction methods based on 2D-Gabor filters have already been used successfully in robust automatic speech recognition (ASR) systems. Recently it was shown that a Mel Frequency Cepstral Coefficients (MFCC) baseline can be improved with physiologically motivated features extracted by a 2D-Gabor filter bank (GBFB). Besides physiologically inspired approaches to ...
متن کاملNoise robust speaker verification with delta cepstrum normalization
This paper introduces a delta cepstrum normalization (DCN) technique for speaker verification under noisy conditions. Cepstral feature normalization techniques are widely used to mitigate spectral variations caused by various types of noise; however, little attention has been paid to normalizing delta features. A DCN technique that normalizes not only base features but also delta-features was r...
متن کاملCompensating Acoustic Mismatch Using Class-Based Histogram Equalization for Robust Speech Recognition
A new class-based histogram equalization method is proposed for robust speech recognition. The proposed method aims at not only compensating for an acoustic mismatch between training and test environments but also reducing the two fundamental limitations of the conventional histogram equalization method, the discrepancy between the phonetic distributions of training and test speech data, and th...
متن کاملHistogram equalization of real and imaginary modulation spectra for noise-robust speech recognition
Histogram equalization (HEQ) of acoustic features has received considerable attention in the area of robust speech recognition because of its relative simplicity and good empirical performance. This paper presents a novel HEQbased feature extraction approach that performs equalization in both acoustic frequency and modulation frequency domains for obtaining better noise-robust features. In part...
متن کامل