The automatic speaker verification spoofing and countermeasures challenge 2015 provides a common framework for the evaluation of spoofing countermeasures or anti-spoofing techniques in the presence of various seen and unseen spoofing attacks. This contribution proposes a system consisting of amplitude, phase, linear prediction residual, and combined amplitude phase-based countermeasures for the...

identifying an unknown language from the test utterances. In this paper, a new method of feature extraction, viz., Teager Energy Based Mel Frequency Cepstral Coefficients (T-MFCC) is developed for identification of perceptually similar languages. Finally, an LID system is presented for Hindi and Urdu (perceptually similar Indian languages) to demonstrate effectiveness of newly proposed feature ...

We describe a perceptual space for timbre, define an objective metric that takes into account perceptual orthogonality and measure the quality of timbre interpolation. We discuss two timbre representations and measure perceptual judgments. We determine that a timbre space based on Mel-frequency cepstral coefficients (MFCC) is a good model for perceptual timbre space.

We describe a perceptual space for timbre, define an objective metric that takes into account perceptual orthogonality and measure the quality of timbre interpolation. We discuss two timbre representations and measure perceptual judgments on an equivalent range of timbre variety. We determine that a timbre space based on Mel-frequency cepstral coefficients (MFCC) is a good model for a perceptua...

This work investigates to improve the robustness of the speaker identification systems based on a modified version of Principal Component Analysis (PCA) and Continuous Wavelet Transform (CWT). Therefore, this work proposes a robust feature extraction method based on MPCA instead of Mel Frequency Cepstral Coefficient (MFCC) that is used in the literature, which is based on converting the common ...

This paper implements a new leukemia identification method which depends on Mel frequency cepstral coefficient (MFCC) feature extraction and wavelet transform. Leukemia identification is a measurement of blood cell features for detecting the blood cancer of a patient. Blood cell feature extraction is based on transforming the blood cell two dimensional (2D) image into one dimensional (1D) signa...

Automatic recognition of the speech of children is a challenging topic in computer-based speech recognition systems. Conventional feature extraction method namely Mel-frequency cepstral coefficient (MFCC) is not efficient for children’s speech recognition. This paper proposes a novel fuzzy-based discriminative feature representation to address the recognition of Malay vowels uttered by children...

This paper investigates the adaptation of automatic speech recognition to disease detection by analyzing the voice parameters. The analysis of the voice allows the identification of the diseases which affect the vocal apparatus and currently is carried out from an expert doctor through methods based on the auditory analysis. This paper presents a novel method to keep track of patient’s patholog...

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...