Speech recognition is a method of finding similarity between two sequences. Various researches have been done on it. In our research, we are trying to achieve the optimal accuracy during the recognition procedure. Here, we are extracting features of the voice sample before filtering it through a noise reduction filter. For each individual, there are number of features are taken using feature ex...

This paper presents a brief survey on Automatic Voice Recognition so as to provide a technological perspective and an appreciation of the fundamental progress that has been accomplished in area of voice communication. The voice is a signal of infinite information. After years of research and development the accuracy of automatic voice recognition remains one of the important research challenges...

This paper describes a speaker verification system which uses two complementary acoustic features: Mel-frequency cepstral coefficients (MFCC) and wavelet octave coefficients of residues (WOCOR). While MFCC characterizes mainly the spectral envelope, or the formant structure of the vocal tract system, WOCOR aims at representing the spectro-temporal characteristics of the vocal source excitation....

The goal of speech parameterization is to extract the relevant information about what is being spoken from the audio signal. In speech recognition systems Mel-Frequency Cepstral Coefficients (MFCC) and Relative Spectral Mel-Frequency Cepstral Coefficients (RASTA-MFCC) are the two main techniques used. It will be shown in this paper that it presents some modifications to the original MFCC method...

acoustic analysis is a proper method in vocal fold pathology diagnosis so that itcan complement and in some cases replace the other invasive, based on direct vocalfold observation, methods. there are different approaches and algorithms for vocalfold pathology diagnosis. these algorithms usually have three stages which arefeature extraction, feature reduction and classification. in this paper in...

The performance of traditional mel-frequency cepstral coefficients (MFCC) speech feature extraction method decreases drastically in the complex noisy environment. To improve the performance and robustness of speech recognition system, which is based on spectral envelope estimation method, the minimum distortionless response spectrum MVDR-MFCC (Minimum Variance Distortionless Response-MFCC) feat...

We examined variants of MFCC and PLP cepstral parameterisations in the context of large vocabulary continuous speech recognition under di erent acoustical environmental conditions: Compared to MFCC, mel-frequency PLP uses a cubic root intensity-toloudness law, and an LPC analysis is applied to the mel-warped spectrum. In LPC-smoothed MFCC, the only di erence to MFCC is the additional LPC smooth...

• For the first time, automatic detection of heart failure (HF) from speech is studied. Both vocal tract and glottal source parameters are used in feature extraction. Four machine learning algorithms as classifiers. Applying Feature selection on + features improved classification accuracy. Among classifiers, neural network gave best performance. Heart a major global health concern increasing pr...

This study proposes using units smaller than words, such as phonemes and syllables, as base units for speech recognition. The system presented here was developed with a hierarchical recognition logic based on the production characteristics of phonemes in Brazilian Portuguese. Decisions are made by Support Vector Machine neural networks grouped to form Specialist Machines. The descriptors used w...