Hamid R. Chinaei

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Spoken language communication between human and machines has become a challenge in research and technology. In particular, enabling the health care robots with spoken language interface is of great attention. Recently due to uncertainty characterizing dialogues, there has been interest for modelling the dialogue manager of spoken dialogue systems using(More)
In this paper, we propose an algorithm for learning a reward model from an expert policy in partially observable Markov decision processes (POMDPs). The problem is formulated as inverse reinforcement learning (IRL) in the POMDP framework. The proposed algorithm then uses the expert trajectories to find an unknown reward model-based on the known POMDP model(More)
The SmartWheeler project aims at developing an intelligent wheelchair for handicapped people. In this paper, we model the dialogue manager of SmartWheeler in MDP and POMDP frameworks using its collected dialogues. First, we learn the model components of the dialogue MDP based on our previous works. Then, we extend the dialogue MDP to a dialogue POMDP, by(More)
To support the personalization of Question Answering (QA) systems, we propose a new probabilistic scoring approach based on the topics of the question and candidate answers. First, a set of topics of interest to the user is learned based on a topic modeling approach such as Latent Dirichlet Allocation. Then, the similarity of questions asked by the user to(More)
A common problem in spoken dialogue systems is finding the intention of the user. This problem deals with obtaining one or several topics for each transcribed, possibly noisy, sentence of the user. In this work, we apply the recent unsupervised learning method, Hidden Topic Markov Models (HTMM), for finding the intention of the user in dialogues. This(More)
Different modes of vibration of the vocal folds contribute significantly to the voice quality. The neutral mode phonation, often used in a modal voice, is one against which the other modes can be contrastively described, also called non-modal phonations. This paper investigates the impact of non-modal phonation on phonological posteriors, the probabilities(More)
Information from different bio-signals such as speech, handwriting, and gait have been used to monitor the state of Parkinson's disease (PD) patients, however, all the multimodal bio-signals may not always be available. We propose a method based on multi-view representation learning via generalized canonical correlation analysis (GCCA) for learning a(More)