This paper investigated the robustness of Teager Energy Cepstrum Coefficient (TECC) in differentiating emotion categories for speech at different White Gaussian noise levels by comparing the performance with MFCC. Experiments involved the normalized squared error measurement, the multi-classes (four classes) emotion classification and the pair-wise emotion classification. This study included fo...

In this paper we present a fast and efficient match algorithm, which consists of two key techniques: Spectral Correlation Based Feature Merge(SCBFM) and Two-Step Retrieval(TSR). SCBFM can remove the redundant information. In consequence, the resulting feature sequence has a smaller size, requiring less storage and computation. In addition, most of the tempo variation is removed; thus a much sim...

Physiological research reported that certain frog species contain antimicrobial substances which is potentially and beneficial in overcoming certain health problem. As a result, there is an imperative need for an automated frog species identification to assist people in physiological research in detecting and localizing certain frog species. This project aims to develop a frog sound identificat...

This paper proposes a method of predicting the formant frequencies of a frame of speech from its mel-frequency cepstral coefficient (MFCC) representation. Prediction is achieved through the creation of a Gaussian mixture model (GMM) which models the joint density of formant frequencies and MFCCs. Using this GMM and an input MFCC vector, a maximum a posteriori (MAP) prediction of the formant fre...

This paper presents a novel automatic speaker age and gender identification approach which combines five different methods at the acoustic level to improve the baseline performance. The five subsystems are (1) Gaussian mixture model (GMM) system based on mel-frequency cepstral coefficient (MFCC) features, (2) Support vector machine (SVM) based on GMM mean supervectors, (3) SVM based on GMM maxi...

We propose a new scheme to reduce phase sensitivity in independent component analysis (ICA)-based feature extraction using an analytical description of the ICAadapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a spectral-domain ICA stage that removes redundant time shift information. The performance of the new scheme is evalu...

We apply the technique of independent component analysis to Fourier power coefficients of speech signal frames for a blind detection of basic vectors (sources). A subset of sources corresponding to the noisy influence of basic frequency is identified and its corresponding features could be eliminated. The mixing coefficients for such sources are then determined for every speech sample. We compa...

This study focuses on the detection of shouted speech in realistic noisy conditions. An automatic system based on modified mel frequency cepstral coefficient (MFCC) feature extraction and Gaussian mixture model (GMM) classification is developed. The performance of the automatic system is compared against human perception measured by a listening test. At moderate noise levels, the automatic syst...

Javanese is an Indonesian culture which needs to be preserved, but many students make mistakes in the pronunciation of letters and find it difficult analyze errors by human teachers because limited time subjective assessment, so a system needed detect incorrect letters. Mispronunciation detection has been widely applied foreign languages, not implemented for carakan This research develops mispr...