Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction matching methods to analyze synthesize these signals. One most commonly used for is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs successful in processing voice signal with high accuracies. represents a sequence signal-specific...

This paper presents experimental results on whispered speech recognition based Teager Energy Operator for linear and mel cepstral coefficients including the Cepstral Mean Subtraction normalization technique. The feature vectors taken into consideration are Linear Frequency Coefficients, Mel Coefficients Coefficients. A speaker dependent scenario is used. For process, Dynamic Time Warping Hidden...

The number of channels is one the important criteria in regard to digital audio quality. Generally, stereo with two can provide better perceptual quality than mono audio. To seek illegal commercial benefit, might convert a system fake Identifying stereo-faking lesser-investigated forensic issue. In this paper, faking corpus first presented, which created using Haas effect technique. Two identif...

Enhancing the performance of emotional speaker recognition process has witnessed an increasing interest in the last years. This paper highlights a methodology for speaker recognition under different emotional states based on the multiclass Support Vector Machine (SVM) classifier. We compare two feature extraction methods which are used to represent emotional speech utterances in order to obtain...

In this paper we introduce a robust feature extractor, dubbed as Modified Function Cepstral Coefficients (MODFCC), based on gammachirp filterbank, Relative Spectral (RASTA) and Autoregressive Moving-Average (ARMA) filter. The goal of this work is to improve the robustness of speech recognition systems in additive noise and real-time reverberant environments. In speech recognition systems Mel-Fr...

Abstract Aiming at the issue that recognition accuracy of traditional acoustic signal features is low for helicopter signals with wind noise in near field, a method extracting mixed MFCC+GFCC based on wavelet decomposition proposed. Firstly, three-layer and reconstruction are applied to signals; then, Mel-Frequency Cepstral Coefficients (MFCC) Gammatone-Frequency Cepstrum Coefficient (GFCC) res...

In this paper, we describe a prototype speaker identification system using auto-associative neural network (AANN) and formant features. Our experiments demonstrate that formants extracted from difference spectrum perform significantly better than formants extracted from normal spectrum for the task of speaker identification. We also demonstrate that formants from difference spectrum provide com...

Speaker normalization is a process in which the short-time features of speech from a given speaker are transformed so as to better match some speaker independent model. Vocal tract length normalization (VTLN) is a popular speaker normalization scheme wherein the frequency axis of the short-time spectrum associated with a particular speaker’s speech is rescaled or warped prior to the extraction ...

This paper presents an audio genre detection framework that can be used for a multi-language audio corpus. Cepstral coefficients are considered and analyzed as the feature set for both a language dependent and language independent genre identification (GID) task. Language information is found to increase the overall detection accuracy on an average by at least 2.6% from its language independent...