This paper describes a technique of estimating style expressiveness for an arbitrary speaker’s emotional speech. In the proposed technique, the style expressiveness, representing how much the emotions and/or speaking styles affect the acoustic features, is estimated based on multiple-regression hidden semiMarkov model (MRHSMM). In the model training, we first train average voice model using mul...

Segmentation and labeling of high dimensional time series data has wide applications in behavior understanding and medical diagnosis. Due to the difficulty of obtaining a large amount the label information, realizing this objective in an unsupervised way is highly desirable. Hidden Semi-Markov Model (HSMM) is a classical tool for this problem. However, existing HSMM and its variants typically m...

Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov ...

Article history: Received 14 April 2009 Available online 17 November 2009

Hidden Markov Models have been used frequently in the audio domain to identify underlying musical structure. Much less work has been done in the purely symbolic realm. Recently, a substantial amount of expertlabelled symbolic musical data has been injected into the research community. The new availability of data allows for the application of machine learning models to purely symbolic tasks. Si...

Today’s state-of-the-art speech recognition systems typically use continuous density hidden Markov models with mixture of Gaussian distributions. Such speech recognition systems have problems; they require too much memory to run, and are too slow for large vocabulary applications. Two approaches are proposed for the design of compact acoustic models, namely, subspace distribution clustering hid...

This paper describes a technique for modeling and controlling emotional expressivity of speech in HMM-based speech synthesis. A problem of conventional emotional speech synthesis based on HMM is that the intensity of an emotional expression appearing in synthetic speech completely depends on the database used for model training. To take into account the emotional expressivity that listeners act...

Behavioral models of human operators engaged in complex, time-critical high-risk domains, such as those typical in Human Supervisory Control (HSC) settings, are of great value because of the high cost of operator failure. We propose that Hidden Semi-Markov Models (HSMMs) can be employed to model behaviors of operators in HSC settings where there is some intermittent human interaction with a sys...

This paper describes spontaneous dialogue speech synthesis based on multiple-regression hidden semi-Markov model (MRHSMM), which enables users to specify paralinguistic information of synthesized speech with a dimensional representation. Paralinguistic aspects of synthesized speech are controlled by multiple regression models whose explanatory variables are abstract dimensions such as pleasant-...