We investigated neural mechanisms that support voice recognition in a training paradigm with fMRI. The same listeners were trained on different weeks to categorize the mid-regions of voice-morph continua as an individual's voice. Stimuli implicitly defined a voice-acoustics space, and training explicitly defined a voice-identity space. The pre-defined centre of the voice category was shifted fr...

This paper presents a new direction for managing the speech recognition errors of a computer with spoken dialogue functions. Our approach uses an implicit control of human voice prosody by focusing on human behavior characteristics, i.e., interactional synchrony at the prosodic level as one of errorprevention methods. In this paper, we introduce a simple experiment to examine the same prosodic ...

This paper describes the correlations between various acoustic features and perceptual voice quality similarity. We focus on identifying the acoustic features that are correlated with voice quality similarity. First, a large-scale perceptual experiment using the voices of 62 speakers is conducted and perceptual similarity scores between each pair of speakers are acquired. Next, multiple linear ...

In this paper, we investigate the effectiveness of speaker adaptation for various essential components in deep neural network based speech synthesis, including acoustic models, acoustic feature extraction, and post-filters. In general, a speaker adaptation technique, e.g., maximum likelihood linear regression (MLLR) for HMMs or learning hidden unit contributions (LHUC) for DNNs, is applied to a...

The identification and classification of pathological voice are still a challenging area of research in speech processing. Acoustic features of speech are used mainly to discriminate normal voices from pathological voices. This paper explores and compares various classification models to find the ability of acoustic parameters in differentiating normal voices from pathological voices. An attemp...