Dyadic scheme of wavelet signal decomposition leads to a specific division of frequency bands. It is comparable to mel-frequency division and may be used in effective parameterization of speech signal in recognition systems, speech coding or other speech signal based applications. This paper discusses efficiency of different spectral distance measures applied to wavelet-parameterized speech. Th...

We use tandem features and a fusion of four systems for textdependent speaker verification on the RedDots corpus. In the tandem system, a senone-discriminant neural network provides a low-dimensional bottleneck feature at each frame which are concatenated with a standard Mel-frequency cepstral coefficients (MFCC) feature representation. The concatenated features are propagated to a conventional...

In this work, we investigate the use of discriminative models for automatic speech recognition of subvocalic speech via surface electromyography (sEMG). We also investigate the suitability of multiresolution analysis in the form of discrete wavelet transform (DWT) for sEMG-based speech recognition. We examine appropriate dimensionality reduction techniques for features extracted using different...

The classification performance of an automatic classifier of voice pathology for the detection of normal and pathologic voice types is presented. The proposed classification system is non-intrusive and fully automated. Speech files of sustained phonation of the vowel sound /a/ in the 'Disordered Voice Database Model 4337' provided 631 subjects of both genders (58 normal, 573 pathologic). This d...

Mel-frequency cepstral coefficients (MFCC) have traditionally been used in speaker identification applications. Their use has been extended to speech quality assessment for clinical applications during the last few years. While the significance of such parameters for such an application may not seem clear at first thought, previous research has demonstrated their robustness and statistical sign...

Automatic Speech Recognition (ASR), is the process of converting a speech waveform into the text quite similar to the information being communicated by the speaker. This paper aims to construct a speech recognition system for Tamil language. Mel Frequency Cepstral Coefficients (MFCC) is a commonly used feature extraction technique for speech recognition which is computed by applying DCT to the ...

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...

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...

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...

We propose a new front-end that reflects some aspects of auditory nerve response. Namely, the pattern of synchrony responses observed over auditory nerve fibers associated with F0, F1 and F2 of voiced sounds. The main goal is to get a set of features, which represents those frequency trajectories. These features should be less sensitive to adverse environmental conditions than mel-cepstrum or F...