We attack a woefully under-explored language genre—lyrics in music—introducing a novel hidden Markov model based method for completely unsupervised identifica-tion of rhyme schemes in hip hop lyrics, which to the best of our knowledge, is the first such effort. Unlike previous approaches that use supervised or semi-supervised approaches for the task of rhyme scheme identification, our model doe...

This paper addressed the problem of Out-Of-Vocabulary (OOV) utterance detection in small vocabulary telephone keyword spotting system. We propose a new approach for modeling OOV words in the scenario of a small vocabulary of telephone keyword spotting system. The paper adopt the semi-continuous Hidden Markov Model with multiple codebooks to modeling the keywords. We propose a two pass procedure...

This paper reports recent efforts to apply the speaker-independent SPHINX-H system to the DARPA Wall Street Journal continuous speech recognition task. In SPHINX-H, we incorporated additional dynamic and speaker-normalized features, replaced discrete models with sex-dependent semi-continuous hidden Markov models, augmented within-word triphones with between-word triphones, and extended generali...

The traditional co-training algorithm, which needs a great number of unlabeled examples in advance and then trains classifiers by iterative learning approach, is not suitable for online learning of classifiers. To overcome this barrier, we propose a novel semi-supervised learning algorithm, called MAPACo-Training, by combining the co-training with the principle of Maximum A Posteriori adaptatio...

Morfessor is a family of methods for learning morphological segmentations of words based on unannotated data. We introduce a new variant of Morfessor, FlatCat, that applies a hidden Markov model structure. It builds on previous work on Morfessor, sharing model components with the popular Morfessor Baseline and Categories-MAP variants. Our experiments show that while unsupervised FlatCat does no...

Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dong-chuan Road, Shanghai 200240, PR China General Electric (Shanghai) Corporation, 1800 Cai Lun Road, Shanghai 201203, PR China Department of Mechanical and Industrial Engineering, 842 West Taylor Street, University of Illinois-Chicago, Chicago, IL 60607, USA d Environmenta...

For tasks such as dressing assistance, robots should be able to adapt to different user morphologies, preferences and requirements. We propose a programming by demonstration method to efficiently learn and adapt such skills. Our method encodes sensory information (relative to the human user) and motor commands (relative to the robot actuation) as a joint distribution in a hidden semi-Markov mod...

Recent brain imaging studies have provided new insight into how students are able to extend their previous problem solving skills to new but similar problems. It is still unclear, however, what the basis is of individual differences in their success at transfer. In this study, 75 subjects had been trained to solve a set of mathematical problems before they were put into the fMRI scanner, where ...

In this study, we investigated the information processing stages underlying associative recognition. We recorded EEG data while participants performed a task that involved deciding whether a probe word triple matched any previously studied triple. We varied the similarity between probes and studied triples. According to a model of associative recognition developed in the Adaptive Control of Tho...

We present and compare different approaches for crossvariety speaker transformation in Hidden Semi-Markov Model (HSMM) based speech synthesis that allow for a transformation of an arbitrary speaker’s voice from one variety to another one. The methods developed are applied to three different varieties, namely standard Austrian German, one Middle Bavarian (Upper Austria, Bad Goisern) and one Sout...