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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)
This paper compares two systems for computational morphological analysis of Dutch. Both systems have been independently designed as separate modules in the context of the FLaVoR project, which aims to develop a modular architecture for automatic speech recognition. The systems are trained and tested on the same Dutch morphological database (CELEX), and can(More)
This paper introduces research within the ALADIN project, which aims to develop an as-sistive vocal interface for people with a physical impairment. In contrast to existing approaches , the vocal interface is self-learning, which means it can be used with any language, dialect, vocabulary and grammar. This paper describes the overall learning framework, and(More)
Exemplar-based speech enhancement systems work by decomposing the noisy speech as a weighted sum of speech and noise exemplars stored in a dictionary and use the resulting speech and noise estimates to obtain a time-varying filter in the full-resolution frequency domain to enhance the noisy speech. To obtain the decomposition, exemplars sampled in lower(More)
In this paper, weakly supervised HMM learning is applied to modeling word acquisition towards human-computer interaction with little manual effort. The only imposed supervisory information is initializing the learning algorithms by two labeled data samples per pattern. Experiments on TIDIG-ITS show that our recently proposed algorithm, Baum-Welch learning(More)
We propose a method to transform the on line speech signal so as to comply with the specifications of an HMM-based automatic speech recognizer. The spectrum of the input signal undergoes a vocal tract length (VTL) normalization based on differences of the average third formant F<sub>3</sub>. The high frequency gap which is generated after scaling is(More)
We propose a novel algorithm for graph regularized non-negative matrix factorization (NMF) with &#x2113;<sub>1</sub> normalization based on the Kullback-Leibler divergence. The &#x2113;<sub>1</sub> normalization is imposed to overcome the scaling ambiguity in earlier work on graph regularized NMF (GNMF) in [D. Cai, X. He, J. Han, and T. Huang, &#x201C;Graph(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)
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