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This paper introduces a new extended average magnitude difference function (EAMDF) for noise robust pitch detection. EAMDF involves in sufficient number of averaging for all lag values compared to the original AMDF, and thereby eliminates the falling tendency of the AMDF without emphasizing pitch harmonics at higher lags, which is a severe limitation of other existing improvements of the AMDF. A noise...
Automatic speaker recognition is one of the difficult tasks in the field of computer speech and speaker recognition. Speaker recognition is a biometric process of automatically recognizing who is speaking on the basis of speaker dependent features of the speech signal. Currently, speaker recognition system is an important need for authenticating the personal like other biometrics such as finger prints...
This paper proposes a voiced - unvoiced measure based on the Analytic Signal computation. This voiced - unvoiced feature can be useful for many speech processing applications. For instance, considering speech recognition, it could be incorporated into commonly used acoustic feature vectors, such as for example the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives, in order...
This paper deals with the voiced/unvoiced segmentation of natural speech signals and, inside the voiced intervals of these signals, with the determination of the fundamental frequency f0. Our primary motivation of developing the method presented in this paper is to obtain precise f0 information, this both in terms of frequency for relatively fast evolving events, and in terms of time location of these...
In this paper, we present a new back-end classifier for GMM-LM based language identification systems. Our new proposed system consists of two main parts, mapping matrix and bank of SVMs. These two parts are located in series after GMM-LM system. The mapping matrix, maps the language models' output vectors to a new space in which the languages are more separable than before. Then each SVM in the SVM...
In this paper a novel approach to doubletalk detection (DTD) is presented. This approach uses a modified non-negative matrix factorization (NMF) technique originally developed for monaural sound source separation to perform DTD. The efficacy of this approach is demonstrated through experiments using real room impulse responses (RIRs). The properties of this algorithm are then discussed with reference...
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