Amir Hossein Poorjam

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This paper proposes a novel approach for automatic speaker height estimation based on the i-vector framework. In this method, each utterance is modeled by its corresponding i-vector. Then artificial neural networks (ANNs) and least-squares support vector regression (LSSVR) are employed to estimate the height of a speaker from a given utterance. The proposed(More)
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor(More)
State-of-the-art language recognition systems involve modeling utterances with the i-vectors. However, the uncertainty of the i-vector extraction process represented by the i-vector posterior covariance is affected by various factors such as channel mismatch, background noise, incomplete transformations and duration variability. In this paper, we propose a(More)
The series of language recognition evaluations (LRE’s) conducted by the National Institute of Standards and Technology (NIST) have been one of the driving forces in advancing spoken language recognition technology. This paper presents a shared view of five institutions resulting from our collaboration toward LRE 2015 submissions under the names of I2R,(More)
This article describes the systems jointly submitted by Institute for Infocomm (IR), the Laboratoire d’Informatique de l’Universit du Maine (LIUM), Nanyang Technology University (NTU) and the University of Eastern Finland (UEF) for 2015 NIST Language Recognition Evaluation (LRE). The submitted system is a fusion of nine sub-systems based on i-vectors [1](More)
In this paper, a novel approach for automatic speaker weight estimation from spontaneous telephone speech signals is proposed. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a(More)
Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four(More)
This paper proposes a novel approach for automatic speaker weight estimation from spontaneous telephone speech signals. In this method, each utterance is modeled using the ivector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a(More)
This paper proposes a novel approach for automatic speaker weight estimation from spontaneous telephone speech signals. In this method, each utterance is modeled using the ivector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a(More)
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