In this paper, we propose a technique for estimating the degree or intensity of emotional expressions and speaking styles appearing in speech. The key idea is based on a style control technique for speech synthesis using a multiple regression hidden semi-Markov model (MRHSMM), and the proposed technique can be viewed as the inverse of the style control. In the proposed technique, the acoustic features of spectrum, power, fundamental frequency, and duration are simultaneously modeled using the MRHSMM. We derive an algorithm for estimating explanatory variables of the MRHSMM, each of which represents the degree or intensity of emotional expressions and speaking styles appearing in acoustic features of speech, based on a maximum likelihood criterion. We show experimental results to demonstrate the ability of the proposed technique using two types of speech data, simulated emotional speech and spontaneous speech with different speaking styles. It is found that the estimated values have correlation with human perception.

This paper describes a technique for controlling the degree of expressivity of a desired emotional expression and/or speaking style of synthesized speech in an HMM-based speech synthesis framework. With this technique, multiple emotional expressions and speaking styles of speech are modeled in a single model by using a multiple-regression hidden semi-Markov model (MRHSMM). A set of control parameters, called the style vector, is defined, and each speech synthesis unit is modeled by using the MRHSMM, in which mean parameters of the state output and duration distributions are expressed by multiple-regression of the style vector. In the synthesis stage, the mean parameters of the synthesis units are modified by transforming an arbitrarily given style vector that corresponds to a point in a low-dimensional space, called style space, each of whose coordinates represents a certain specific speaking style or emotion of speech. The results of subjective evaluation tests show that style and its intensity can be controlled by changing the style vector.

This paper proposes a technique for synthesizing speech with a desired speaking style and/or emotional expression, based on model adaptation in an HMM-based speech synthesis framework. Speaking styles and emotional expressions are characterized by many segmental and suprasegmental features in both spectral and prosodic features. Therefore, it is essential to take account of these features in the model adaptation. The proposed technique called style adaptation, deals with this issue. Firstly, the maximum likelihood linear regression (MLLR) algorithm, based on a framework of hidden semi-Markov model (HSMM) is presented to provide a mathematically rigorous and robust adaptation of state duration and to adapt both the spectral and prosodic features. Then, a novel tying method for the regression matrices of the MLLR algorithm is also presented to allow the incorporation of both the segmental and suprasegmental speech features into the style adaptation. The proposed tying method uses regression class trees with contextual information. From the results of several subjective tests, we show that these techniques can perform style adaptation while maintaining naturalness of the synthetic speech.

This paper describes an approach to generating speech with emotional expressivity and speaking style variability. The approach is based on a speaking style and emotional expression modeling technique for HMM-based speech synthesis. We first model several representative styles, each of which is a speaking style and/or an emotional expression, in an HMM-based speech synthesis framework. Then, to generate synthetic speech with an intermediate style from representative ones, we synthesize speech from a model obtained by interpolating representative style models using a model interpolation technique. We assess the style interpolation technique with subjective evaluation tests using four representative styles, i.e., neutral, joyful, sad, and rough in read speech and synthesized speech from models obtained by interpolating models for all combinations of two styles. The results show that speech synthesized from the interpolated model has a style in between the two representative ones. Moreover, we can control the degree of expressivity for speaking styles or emotions in synthesized speech by changing the interpolation ratio in interpolation between neutral and other representative styles. We also show that we can achieve style morphing in speech synthesis, namely, changing style smoothly from one representative style to another by gradually changing the interpolation ratio.

This paper describes the modeling of various emotional expressions and speaking styles in synthetic speech using HMM-based speech synthesis. We show two methods for modeling speaking styles and emotional expressions. In the first method called style-dependent modeling, each speaking style and emotional expression is modeled individually. In the second one called style-mixed modeling, each speaking style and emotional expression is treated as one of contexts as well as phonetic, prosodic, and linguistic features, and all speaking styles and emotional expressions are modeled simultaneously by using a single acoustic model. We chose four styles of read speech -- neutral, rough, joyful, and sad -- and compared the above two modeling methods using these styles. The results of subjective evaluation tests show that both modeling methods have almost the same accuracy, and that it is possible to synthesize speech with the speaking style and emotional expression similar to those of the target speech. In a test of classification of styles in synthesized speech, more than 80% of speech samples generated using both the models were judged to be similar to the target styles. We also show that the style-mixed modeling method gives fewer output and duration distributions than the style-dependent modeling method.