The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...

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 proposes a Mel Frequency Cepstral Coefficient (MFCC) based hybrid algorithm for motor imagery classification of Electroencephalogram (EEG) signal for Brain Computer Interface (BCI). The proposed hybrid algorithm contains MFCC with Hjorth Parameter. Regression coefficient method was used for eye artifacts cancellation. The feature extraction method based on the difference of the diffe...

Existing standard speech coders can provide speech communication of high quality while they degrade the performance of speech recognition systems that use the reconstructed speech by the coders. The main cause of the degradation is that the spectral envelope parameters in speech coding are optimized to speech quality rather than to the performance of speech recognition. For example, mel-frequen...

Nowadays the electronic gadgets have been updated to store large amount of music information. It is necessary to have an efficient retrieval system to choose the required data. The important task in audio retrieval system is feature extraction. In the feature extraction stage, the feature which gives relevant information about music has to be extracted. In this paper, various Mel based feature ...

The report proposes a method for detecting the sound events in a basketball game with focusing on detecting cheering sound. MFCC (Mel-frequency cepstral coefficient) features are used to identify the cheering sounds from speeches and other confusing sounds. The mfcc features are fed into a neural network and classified into three classes (cheering, speech, and others). To improve the MFCC-NN pe...

Speaker recognition is one of the most essential tasks in the signal processing which identifies a person from characteristics of voices . In this paper we accomplish speaker recognition using Mel-frequency Cepstral Coefficient (MFCC) with Weighted Vector Quantization algorithm. By using MFCC, the feature extraction process is carried out. It is one of the nonlinear cepstral coefficient functio...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...

This work presents an application of Fundamental Frequency (Pitch), Linear Predictive Cepstral Coefficient (LPCC) and Mel Frequency Cepstral Coefficient (MFCC) in identification of sex of the speaker in speech recognition research. The aim of this article is to compare the performance of these three methods for identification of sex of the speakers. A successful speech recognition system can he...