A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...

Automatic Speech Recognition (ASR) technology is a way to interface with computer. In this paper we describe speech recognition technique using multiple codebooks of MFCC derived features. The proposed algorithm is useful in detecting isolated words of speech. In this algorithm we first create database i.e. codebook by calculating mel frequency cepstral coefficient first and then codeword for e...

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice...

In this paper, Radial basis neural networks[1][12][17] have been examined for speech recognition using speech features MFCC (Mel frequency Coefficients) and Gamma tone frequency coefficients for isolated Telugu words in noisy environment. Speech feature vectors are used to train, validate and test the Radial basis neural networks.Experiments conducted in Office environment under the presence of...

Non-verbal vocal interaction (NVVI) is an interaction method in which sounds other than speech produced by a human are used, such as humming. NVVI complements traditional speech recognition systems with continuous control. In order to combine the two approaches (e.g. “volume up, mmm”) it is necessary to perform a speech/NVVI segmentation of the input sound signal. This paper presents two novel ...

Mel Frequency Cepstral Coefficients (MFCC) and Perceptual Linear Prediction (PLP) are the most popular acoustic features used in speech recognition. Often it depends on the task, which of the two methods leads to a better performance. In this work we develop acoustic features that combine the advantages of MFCC and PLP. Based on the observation that the techniques have many similarities, we rev...

Our purpose is to evaluate the efficiency of MPEG-7 basis projection (BP) features vs. Mel-scale Frequency Cepstrum Coefficients (MFCC) for speaker recognition in noisy environments. The MPEG-7 feature extraction mainly consists of a Normalized Audio Spectrum Envelope (NASE), a basis decomposition algorithm and a spectrum basis projection. Prior to the feature extraction the noise reduction alg...