We provide a supervised speech-independent voice recognition technique in this paper. In the feature extraction stage we propose a mel-cepstral based approach. Our feature vector classification method uses a special nonlinear metric, derived from the Hausdorff distance for sets, and a minimum mean distance classifier. Keywords—Text-independent speaker recognition, mel cepstral analysis, speech ...

Language identification is at the forefront of assistance in many applications, including multilingual speech systems, spoken language translation, recognition, and human-machine interaction via voice. The indonesian local languages using technology has enormous potential to advance tourism digital content Indonesia. goal this study identify four Indonesian languages: Javanese, Sundanese, Minan...

Human recognizes speech emotions by extracting features from the speech signals received through the cochlea and later passed the information for processing. In this paper we propose the use of Mel-Frequency Cepstral Coefficient (MFCC) to extract the speech emotion information to provide both the frequency and time domain information for analysis. Since features extracted using the MFCC simulat...

Heart murmur recognition and classification play an important role in the auscultative diagnosis. The method based on hidden markov model (HMM) was presented to recognize the heart murmur. The murmur was isolated on basis of the principle of wavelet analysis considering the time-frequency characteristics of the heart murmur. This method uses Mel frequency cepstral coefficient (MFCC) to extract ...

Speech babble is one of the most challenging noise interference due to its speaker/speech like characteristics for speech and speaker recognition systems. Performance of such systems strongly degrades in the presence of background noise, like the babble noise. Existing techniques solve this problem by additional processing of speech signal to remove noise. In contrast to existing works, the aim...

An auditory-based feature extraction algorithm naming the Basilar-membrane Frequency-band Cepstral Coefficient (BFCC) is proposed to increase the robustness for automatic speech recognition. Compared to Fourier spectrogram based of the MelFrequency Cepstral Coefficient (MFCC) method, the proposed BFCC method engages an auditory spectrogram based on a gammachirp wavelet transform to simulate the...

The mel-cepstral coefficients are often calculated from the linear prediction coefficients by using recursion formulas. However, the obtained mel-cepstral coefficients have errors caused by truncation in the quefrency domain. The purpose of this report is to point out that the melcepstral coefficients can be calculated from the LP (Linear Prediction) coefficients using the recursion formulas wi...

In the study, the effectiveness of combinations of cepstral features, channel compensation techniques, and different local distances in the Dynamic Time Warping (DTW) algorithm is experimentally evaluated in the text-dependent speaker identification task. The training and the testing has been done with noisy telephone speech (short phrases in Bulgarian with length of about 2 seconds) selected f...

Bridge damage detection using vibration data has been confirmed as a promising approach. Compared to the traditional method that typically needs install sensors or systems directly on bridges, drive-by bridge gained increasing attention worldwide since it just one few instrumented passing vehicle. frequencies extracted from vehicle’s vibrations can be good references for detection. However, ext...

This study is intended to develop a noise robust distributed speech recognizer for real-world applications by employing Cepstral Mean Normalization (CMN) for robust feature extraction. The main focus of the work is to cope with different noisy environments. To realize this objective, Mel-LP based speech analysis has been used in speech coding on the linear frequency scale by applying a first-or...