We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients(MFCC). For the classification we also have two choices: Maximum Likelihood Hidden Markov Models(ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the m...

This paper describes the development of an efficient speech recognition system using different techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and Hidden Markov Model (HMM). This paper explains how speaker recognition followed by speech recognition is used to recognize the speech faster, efficiently and accurately. MFCC is used to extract the characterist...

In speech recognition, a speech/non-speech detection must be robust to noise. In this work, a new method for speech/nonspeech detection using a Linear Discriminant Analysis (LDA) applied to Mel Frequency Cepstrum Coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects...

Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step ...

Continuous speech recognition applications need precise detection because the number of words to recognize is unknown and vocabulary words can be short. The speech/non-speech detection must be robust to the boundary precision. In this work, a new approach to evaluate detection algorithm for continuous speech recognition is presented. The speech/non-speech detection using energy parameter combin...

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 ...

This paper presents a quantitative metric to describe the multidimensionality of spectral envelope perception, that is, the perception specifically related to the spectral element of timbre. Mel-cepstrum (Mel-frequency cepstral coefficients or MFCCs) is chosen as a hypothetical metric for spectral envelope perception due to its desirable properties of linearity, orthogonality, and multidimensio...

Speaker verification and identification systems most often employ HMMs and GMMs as recognition engines. This paper describes an algorithm for the optimal selection of the feature space, suitable for these engines. In verification systems, each speaker (target) is assigned an “individual” optimal feature space in which he/she is best discriminated against impostors. Several feature selection pro...

This paper presents a method that combines the techniques of temporal trajectory filtering and projection measure for robust speaker identification. The proposed robust feature, called Relative Autocorrelation Sequence Mel-scale Frequency Cepstral Coefficients (RAS-MFCC), is derived based on filtering the temporal trajectories of short-time one-sided autocorrelation sequences. This filtering pr...

Mel-frequency cepstrum coefficients (MFCCs) extracted from speech recordings has been proven to be the most effective feature set to capture the frequency spectra produced by a recording device. This paper claims that audio evidence such as a recorded call contains intrinsic artifacts at both transmitting and receiving ends. These artifacts allow recognition of the source mobile device on the o...