This paper introduces a generative model of voice fundamental frequency (F0) contours that allows us to extract prosodic features from raw speech data. The present F0contour model is formulated by translating the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration, into a probabilistic model described as a discrete-time stochastic process. There are two motivations behind this formulation. One is to derive a general parameter estimation framework for the Fujisaki model that allows the introduction of powerful statistical methods. The other is to construct an automatically trainable version of the Fujisaki model that we can incorporate into statistical-model-based text-to-speech synthesizers in such a way that the Fujisaki-model parameters can be learned from a speech corpus in a unified manner. It could also be useful for other speech applications such as emotion recognition, speaker identification, speech conversion and dialogue systems, in which prosodic information plays a significant role. We quantitatively evaluated the performance of the proposed Fujisaki model parameter extractor using real speech data. Experimental results revealed that our method was superior to a state-of-the-art Fujisaki model parameter extractor.

We have previously proposed a generative model of speech F0 contours, based on the discrete-time version of the Fujisaki model (a model of the mechanisim for controlling F0s through laryngeal muscles). One advantage of this model is that it allows us to apply statistical methods to estimate the Fujisakimodel parameters from speech F0 contours. This paper proposes a new generative model of speech F0 contours incorporating a vocabulary model of intonation patterns. A parameter inference algorithm for the present model is derived. We quantitatively evaluated the performance of our parameter inference algorithm.

This paper introduces our ongoing work on generative modeling of speech fundamental frequency (F0) contours for estimating prosodic features from raw speech data. The present F0 contour model is formulated by translating the Fujisaki model, a well-founded mathematical model representing the control mechanism of vocal fold vibration, into a probabilistic model described as a discrete-time stochastic process. The motivation behind this formulation is two fold. One is to derive a general parameter estimation framework for the Fujisaki model, allowing for the introduction of powerful statistical methods. The other is to construct an automatically trainable version of the Fujisaki model so that in future it can be used to develop a statistical speaking style conversion system or incorporated into existing text-to-speech synthesis systems to improve the naturalness and intelligibility of computer-generated speech. We also briefly introduce a generative model of F0 contours of singing voice developed under the same spirit. Index Terms: speech F0 contour, Fujisaki model, generative model, hidden Markov model, EM algorithm

This paper proposes a stochastic model of speech F0 contours, based on the stochastic formulation of the Fujisaki model. Our motivation for the stochastic formulation is twofold. Firstly, it allows us to derive a well-behaved algorithm for estimating the Fujisaki model parameters from a raw F0 contour. Secondly, it will open the door to incorporating the well-founded F0 contour model into various statistical speech processing problems. We quantitatively evaluated the performance of our method in terms of an Fujisaki-model parameter estimation accuracy using real speech data. Experimental results revealed that our method was superior to a state-of-the-art Fujisaki model parameter extractor. Index Terms: speech F0 contours, statistical model, Fujisaki model, hidden Markov model, EM algorithm