A Study of Low-variance Multi-taper Features for Distributed Speech Recognition
نویسندگان
چکیده
In this paper we study low-variance multi-taper spectrum estimation methods to compute the mel-frequency cepstral coefficient (MFCC) features for robust speech recognition. In speech recognition, MFCC features are usually computed from a Hamming-windowed DFT spectrum. Although windowing helps in reducing the bias of the spectrum, but variance remains high. Multitaper spectrum estimation methods can be used to correct the shortcomings of single taper (or window) spectrum estimation methods. Experimental results on the AURORA-2 corpus show that the multi-taper methods, specifically the multi-peak multi-taper method, perform better compared to the Hammingwindowed spectrum estimation method.
منابع مشابه
Multitaper MFCC and PLP features for speaker verification using i-vectors
In this paper we study the performance of the low-variance multi-taper Mel-frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) features in a state-ofthe-art i-vector speaker verification system. The MFCC and PLP features are usually computed from a Hamming-windowed periodogram spectrum estimate. Such a singletapered spectrum estimate has large variance, which can be red...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کاملClassification of emotional speech using spectral pattern features
Speech Emotion Recognition (SER) is a new and challenging research area with a wide range of applications in man-machine interactions. The aim of a SER system is to recognize human emotion by analyzing the acoustics of speech sound. In this study, we propose Spectral Pattern features (SPs) and Harmonic Energy features (HEs) for emotion recognition. These features extracted from the spectrogram ...
متن کاملEEG Based Brain Computer Interface Hand Grasp Control: Feature Extraction Method MTCSP
Brain-Computer Interfaces (BCIs) are communication systems, which enable users to send commands to computers by using brain activity only; this activity being generally measured by Electroencephalography (EEG). BCIs are generally designed according to a pattern recognition approach, i.e., by extracting features from EEG signals, and by using a classifier to identify the user’s mental state from...
متن کاملAn Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition
Convolutional Neural Networks (CNNs) have been shown their performance in speech recognition systems for extracting features, and also acoustic modeling. In addition, CNNs have been used for robust speech recognition and competitive results have been reported. Convolutive Bottleneck Network (CBN) is a kind of CNNs which has a bottleneck layer among its fully connected layers. The bottleneck fea...
متن کامل