This paper proposes a Bayesian approach to hidden semiMarkov model (HSMM) based speech synthesis. Recently, hidden Markov model (HMM) based speech synthesis based on the Bayesian approach was proposed. The Bayesian approach is a statistical technique for estimating reliable predictive distributions by treating model parameters as random variables. In the Bayesian approach, all processes for con...

This paper describes an extension of maximum likelihood linear regression (MLLR) to hidden semi-Markov model (HSMM) and presents an adaptation technique of phoneme/state duration for an HMM-based speech synthesis system using HSMMs. The HSMM-based MLLR technique can realize the simultaneous adaptation of output distributions and state duration distributions. We focus on describing mathematical ...

In control vector-based expressive speech synthesis, the emotion/style control vector defined in the categorical (CAT) emotion space is uneasy to be precisely defined by the user to synthesize the speech with the desired emotion/style. This paper applies the arousal-valence (AV) space to the multiple regression hidden semi-Markov model (MRHSMM)-based synthesis framework for expressive speech sy...

This article concerns the study of the asymptotic properties of the maximum likelihood estimator (MLE) for the general hidden semi-Markov model (HSMM) with backward recurrence time dependence. By transforming the general HSMM into a general hidden Markov model, we prove that under some regularity conditions, the MLE is strongly consistent and asymptotically normal. We also provide useful expres...

We examine the use of hidden Markov and hidden semi-Markov models for automatically segmenting an electrocardiogram waveform into its constituent waveform features. An undecimated wavelet transform is used to generate an overcomplete representation of the signal that is more appropriate for subsequent modelling. We show that the state durations implicit in a standard hidden Markov model are ill...

We compare a hidden Markov and Erlang semi-Markov modeling of a partially observable deteriorating system operating under a varying load and subject to multi-sensor vibration monitoring. The evolution of the unknown state process is described by a hidden, two state semi-Markov process with an Erlang sojourn time distribution in the healthy state. The unknown model parameters are estimated using...

In this paper we apply adaptive modeling methods in Hidden Semi-Markov Model (HSMM) based speech synthesis to the modeling of three different varieties, namely standard Austrian German, one Middle Bavarian (Upper Austria, Bad Goisern), and one South Bavarian (East Tyrol, Innervillgraten) dialect. We investigate different adaptation methods like dialectadaptive training and dialect clustering th...

Singing voice is specific in music: a vocal performance conveys both music (melody/pitch) and lyrics (text/phoneme) content. This paper aims at exploiting the advantages of melody and lyric information for real-time audio-to-score alignment of singing voice. First, lyrics are added as a separate observation stream into a template-based hidden semi-Markov model (HSMM), whose observation model is...

In this paper we discuss the most recent evaluation of the RPI language identi cation system by the National Institute of Standards and Technologies (NIST). This system is based on an acousto-phonetic approach where the phonemes present in a language are identi ed by a hidden semi-Markov model (HSMM). The HSMM was also developed at RPI. Knowledge of these phonemes provides us with the necessary...