Phonetically balanced texts are used to study different voice and speech characteristics. In the context of clinical work and research, these texts provide a standard for quantifying perceptual, acoustic, or aerodynamic assessments. Recent modeling efforts are being devoted to describing long-term speech behaviors based on a collection of sustained phonemes. However, comprehensive descriptions of phoneme distributions representative of connected speech are not readily available. Thus, the present study introduces a method to estimate phoneme distributions using text data mining, as an alternative to existing power law methods. The procedure used for the decomposition of texts into phonemes, the estimation of the phonetic distributions and the comparisons between different texts, conversational speech, and standard reading passages are discussed. The results are presented using histograms and R-squared determination coefficients for the case of the English language, although the approach can be easily applied for other languages. A discussion of the proposed method, results, and limitations is presented.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.