Deep Neural Network (DNN) have been extensively used in Automatic Speech Recognition (ASR) applications. Very recently, DNNs have also found application in detecting natural vs. spoofed speech at ASV spoof challenge held at INTERSPEECH 2015. Along the similar lines, in this work, we propose a new feature extraction architecture of DNN called the subband autoencoder (SBAE) for spoof detection ta...

Economical speaker recognition solution from degraded human voice signal is still a challenge. This article covering results of an experiment which targets to improve feature extraction method for effective identification audio with the help data science. Every speaker’s has identical characteristics. Human ears can easily identify these different characteristics and classify audio. Mel-Frequen...

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 ...

Similarity measurement is an important part of speaker identification. This study has modified the similarity measurement technique performed in previous studies. Previous studies used the sum of the smallest distance between the input vectors and the codebook vectors of a particular speaker. In this study, the technique has been modified by selecting a particular speaker codebook which has the...

In this paper, a novel pitch mean based frequency warping (PMFW) method is proposed to reduce the pitch variability in speech signals at the frontend of speech recognition. The warp factors used in this process are calculated based on the average pitch of a speech segment. Two functions to describe the relations between the frequency warping factor and the pitch mean are defined and compared. W...

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...