Detecting a targeted voice style in an audiobook using voice quality features

Audiobooks are known to contain a variety of expressive speaking styles that occur as a result of the narrator mimicking a character in a story, or expressing affect. An accurate modeling of this variety is essential for the purposes of speech synthesis from an audiobook. Voice quality differences are important features characterizing these different speaking styles, which are realized on a gradient and are often difficult to predict from the text. The present study uses a parameter characterizing breathy to tense voice qualities using features of the wavelet transform, and a measure for identifying creaky segments in an utterance. Based on these features, a combination of supervised and unsupervised classification is used to detect the regions in an audiobook, where the speaker changes his regular voice quality to a particular voice style. The target voice style candidates are selected based on the agreement of the supervised classifier ensemble output, and evaluated in a listening test.