An algorithm designed to enhance the intelligibility of speech signals before they are presented in noisy environments was evaluated. The processed and unprocessed speech had the same root-mean square level. Spectral energy was redistributed to increase the signal-to-noise ratio (SNR) in the mid- and high-frequency bands, while the softer segments of speech were increased in level by applying time-domain dynamic range compression. Noise level adaptation was introduced to increase the subjective quality of signals at high SNRs. Evaluations were conducted both in near field (headphones) and in far field (outdoor) conditions using listeners with normal hearing and two types of background. The results showed: (a) In the near field test, the proposed algorithm yielded significant intelligibility improvements relative to the unprocessed speech for both stationary and nonstationary backgrounds; (b) In the far field test, the proposed algorithm increased the intelligibility of unprocessed speech by a factor of seven at 200-m distance from the sound source; and (c) When the background level did not alter intelligibility, listeners preferred the quality of the speech processed by the noise-dependent version of the algorithm.

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.

This paper deals with the problem of generating the fundamental frequency (F0) contour of speech from a text input for text-to-speech synthesis. We have previously introduced a statistical model describing the generating process of speech F0 contours, based on the discrete-time version of the Fujisaki model. One remarkable feature of this model is that it has allowed us to derive an efficient algorithm based on powerful statistical methods for estimating the Fujisaki-model parameters from raw F0 contours. To associate a sequence of the Fujisakimodel parameters with a text input based on statistical learning, this paper proposes extending this model to a context-dependent one. We further propose a parameter training algorithm for the present model based on a decision tree-based context clustering. Index Terms: Speech F0 contours, stochastic model, Fujisaki model, hidden Markov model, EM algorithm

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