Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, detection useful in order to attenuate mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coef...

This paper presents an evaluation of the RWTH large vocabulary speech recognition system on the Aurora 4 noisy Wall Street Journal database. First, the influence of different root functions replacing the logarithm in the feature extraction is studied. Then quantile based histogram equalization is applied, a parametric method to increase the noise robustness by reducing the mismatch between the ...

We investigated a robust speech feature extraction method using kernel PCA (Principal Component Analysis) for distorted speech recognition. Kernel PCA has been suggested for various image processing tasks requiring an image model, such as denoising, where a noise-free image is constructed from a noisy input image [1]. Much research for robust speech feature extraction has been done, but it rema...

, (p s c S p th cepstral coefficient of the s th sub-bands { c 1 (1,p) = c(p) is the p th full-band cepstral parameter} S number of sub-bands Y(k) k th log spectral magnitude K number of log spectral magnitudes) (k Y ′ ′ k th log-energy outputs of the mel-scale filterbank K ′ ′ number of log-energy outputs of the mel-scale filterbank h t weight associated with the t th segment U number of compe...

This project's 'HMM Based Automatic Speech Recognition Analysis main motive is just to generate an Automatic speech recognition which is clear an accurate using Hidden Markov Model (HMM) to get accurate results at number of frequency ranges related to human voice. Here is a record of 12 different words which is recorded by using a number of different speakers that includes male and female both ...

The electrocardiogram is traditionally used to diagnose a large number of heart pathologies. Research improve the readability and classification cardiac signals includes studies geared toward sonification electrocardiographic signal others involving features related music processing, such as Mel-frequency cepstral coefficients. In terms processing features, this study seeks use information retr...

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts YouTube. Some of methods in study are pitch shifting for augmentation, audio data, basic statistics taking mean, median, min, max, standard deviation e...

Speech recognition is an important field of digital signal processing. Automatic Speaker Recognition (ASR) objective is to extract features, characterize and recognize speaker. Mel Frequency Cepstral Coefficients (MFCC) is most widely used feature vector for ASR. MFCC is used for designing a text dependent speaker identification system. In this paper the DSP processor TMS320C6713 with Code Comp...

This paper suggests Digital Signal processor (DSP) based speech recognition system with improved performance in terms of recognition accuracies and computational cost. The comprehensive surrey of various approaches of feature extraction like Mel filter banks with Mel Frequency Cepstrum Coefficients (MFCC). This paper describes an approach of isolated speech recognition by Digital Signal Process...