Mel-frequency cepstral coefficients are used as an abstract representation of the spectral envelope of a given signal. Although they have been shown to be a powerful descriptor for speech and music signals, more accurate and easily interpretable options can be devised. In this study, we present and evaluate the shape-based spectral contrast descriptor, which is build up from the previously prop...

The transmission of audio data via the Internet Things makes such vulnerable to tampering. Moreover, availability sophisticated tampering tools has allowed mobsters change context by altering their segments. Tampered may result in unpleasant situations for any member society. To avoid circumstances, a new forgery detection system is proposed this study. This can be deployed edge devices identif...

Filter bank is the most common feature being employed in the research of the marginalisation approaches for robust speech recognition due to its simplicity in detecting the unreliable data in the frequency domain. In this paper, we propose a hybrid approach based on the marginalisation and the soft decision techniques that make use of the Mel-frequency cepstral coefficients (MFCCs) instead of f...

Voice Activity Detection (VAD) algorithms based on machine learning techniques have shown competitive results in the area of automatic speech recognition. This paper describes a new approach of VAD based on Support Vector Machines (SVM) for Distributed Speech Recognition (DSR) system. In the proposed scheme, the speech and the non-speech frames are detected from the compressed Mel Frequency Cep...

This paper describes a medium size Bangla speech corpus preparation and the comparison of the performances of different acoustic features for Bangla word recognition. A small number of speakers are use for most of the Bangla automatic speech recognition (ASR) system, but 40 speakers selected from a wide area of Bangladesh, where Bangla is used as a native language, are involved here. In the exp...

Automatic speech recognition by machine is one of the most efficient methods for man-machine communications. Because speech waveform is nonlinear and variant. Speech recognition requires a lot of intelligence and fault tolerance in the pattern recognition algorithms. Accurate vowel recognition forms the backbone of most successful speech recognition systems. A collection of techniques exists to...

Conventional automatic speaker verification systems are based on cepstral features like Mel-scale Frequency Cepstrum Coefficient (MFCC), or Linear Predictive Cepstrum Coefficient (LPCC). Recent published works showed that the use of complementary features can significantly improve the system performances. In this paper, we propose to use an evolution strategy to optimize the complementarity of ...

Need for deciding whether a given frame of a speech waveform should be classified as voiced speech or unvoiced speech arises in many speech analysis systems. Several approaches have been described in the literature for making this decision. In this paper, we have presented two novel approaches of using acoustical features and pattern recognition. The first method is based on Mel frequency cepst...

Mel-Frequency Cepstral Coefficients are spectral feature which are widely used for speaker recognition and text dependent speaker recognition systems are the most accurate in voice based authentication systems. In this paper, a text dependent speaker recognition method is developed. MFCCs are computed for a selected sentence. The first 13 MFCCs are considered for each frames of duration 26ms an...

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...