Sharif Digital Repository

The duration of utterances is one of the effective factors on the performance of speaker verification systems. Text dependent speaker verification suffers from both short duration and unmatched content between enrollment and test segments. In this paper, we use Discriminative Gaussian Process Latent Variable Model (DGPLVM) to deal with the uncertainty caused by short duration. This is the first attempt to utilize Gaussian Process for speaker verification. Also, to manage the unmatched content between enrollment and test segments we proposed the localized-DGPLVM that trains DGPLVM for each phrase in dataset. Experiments show the relative improvement of 27.4% in EER on RSR2015  

The generalized belief propagation (GBP), introduced by Yedidia et al., is an extension of the belief propagation (BP) algorithm, which is widely used in different problems involved in calculating exact or approximate marginals of probability distributions. In many problems, it has been observed that the accuracy of GBP outperforms that of BP considerably. However, due to its generally higher complexity compared to BP, its application is limited in practice. In this paper, we introduce a stochastic version of GBP called stochastic generalized belief propagation (SGBP) that can be considered as an extension to the stochastic BP (SBP) algorithm introduced by Noorshams et al. They have shown... 

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability... 

Today, the Internet is used to offer different services to users. Most of these services are designed for human users, but unfortunately some computer programs are designed which abuse these services. CAPTCHA (Completely Automated Public Turing test to tell Computers and Human Apart) systems are designed to automatically distinguish between human users and computer programs and block such computer programs. Most of current CAPTCHA methods are using visual patterns and hence blind users cannot use them. In this paper, we propose a new CAPTCHA method which is designed for blind people. In this method, a small sound clip is played for the user and he/she is asked to say a word. Then the user... 

In this paper, we combine Hidden Markov Models HMMs with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform channel and uncertainty compensation, and we propose a novel method for using the uncertainty in the... 

We define a new feature called Teager Energy to Energy and mathematically show that it provides distinguished values for pure tone and white noise signals. We then employ the Teager Energy to Energy feature to propose an efficient procedure for voice activity detection and use simulation results to evaluate its performance in different noisy environments. Furthermore, we experimentally demonstrate the performance of the proposed voice activity detection technique in a real-time voice processing embedded system. Experimental and simulation results show that the introduced procedure provides more reliable results with a reasonable amount of computational complexity in comparison with its... 

Automatic Speaker Verification (ASV) is authentication of individuals by analyzing their speech signals. Different synthetic approaches allow spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a voice or reconstruct the features. Attackers beat up the ASVs using four general techniques; impersonation, speech synthesis, voice conversion, and replay. The last technique is considered as a common and high potential tool for spoofing purposes since replay attacks are more accessible and require no technical knowledge of adversaries. In this study, we introduce a novel replay spoofing countermeasure for ASVs. Accordingly, we use the Constant Q Cepstral Coefficient (CQCC)... 

Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of complex noise detection and removal algorithms, which composes a trade-off between fast or noise robust recognition. This paper presents a novel approach to speech recognition using fuzzy modeling and decision making that ignores noise instead of its detection and removal. To do so, the speech spectrogram... 

This paper intends to introduce a simple fast space-efficient linear method for a general pattern recognition problem. The presented algorithm can find the closest match for a given sample within a number of samples which has already been introduced to the system. The fact of using averaging and fuzzy numbers in this method encourages that it may be a noise resistant recognition process. As a test bed, a problem of recognition of spoken words has been set forth to this algorithm. Test data contain clean and noisy samples and results have been compared to that of a widely used speech recognition method, HMM  

A novel very light weight approach to isolated word speech recognition is introduced. The approach uses a new simplistic feature set and a neural network recognition system. The algorithm's main processing requirements are FFT computation and a simple neural network comparison, making the method a suitable solution for low price embedded devices. The proposed method is tested on single speaker and multiple speaker test sets and the results are compared with a widely used speech recognition approach, presenting very fast recognition and quite good recognition rate. © 2006 IEEE  

Supervised speech emotion recognition requires a large number of labeled samples that limit its use in practice. Due to easy access to unlabeled samples, a new semi-supervised method based on auto-encoders is proposed in this paper for speech emotion recognition. The proposed method performed the classification operation by extracting the information contained in unlabeled samples and combining it with the information in labeled samples. In addition, it employed maximum mean discrepancy cost function to reduce the distribution difference when the labeled and unlabeled samples were gathered from different datasets. Experimental results obtained on different emotional speech datasets... 

A controversial issue in artificial intelligence is human emotion recognition. This paper presents a fuzzy parallel cascades (FPC) model for predicting the continuous subjective emotional appraisal of music by time-varying spectral content of electroencephalogram (EEG) signals. The EEG, along with an emotional appraisal of 15 subjects, was recorded during listening to seven musical excerpts. The emotional appraisement was recorded along the valence and arousal emotional axes as a continuous signal. The FPC model was composed of parallel cascades with each cascade containing a fuzzy logic-based system. The FPC model performance was evaluated using linear regression (LR), support vector... 

In this paper a new method for automatic word clustering is presented. We used this method for building n-gram language models for Persian continuous speech recognition (CSR) systems. In this method, each word is specified by a feature vector that represents the statistics of parts of speech (POS) of that word. The feature vectors are clustered by k-means algorithm. Using this method causes a reduction in time complexity which is a defect in other automatic clustering methods. Also, the problem of high perplexity in manual clustering methods is abated. The experimental results are based on "Persian Text Corpus" which contains about 9 million words. The extracted language models are evaluated... 

Hidden Markov model (HMM)-based minimum mean square error speech enhancement method in Mel-frequency domain is focused on and a parallel cepstral and spectral (PCS) modeling is proposed. Both Mel-frequency spectral (MFS) and Mel-frequency cepstral (MFC) features are studied and experimented for speech enhancement. To estimate clean speech waveform from a noisy signal, an inversion from the Mel-frequency domain to the spectral domain is required which introduces distortion artifacts in the spectrum estimation and the filtering. To reduce the corrupting effects of the inversion, the PCS modeling is proposed. This method performs concurrent modeling in both cepstral and magnitude spectral...