Phone Duration Modeling of Affective Speech Using Support Vector Regression

Improving phone duration modelling using support vector regression fusion

In the present work, we propose a scheme for the fusion of different phone duration models, operating in parallel. Specifically, the predictions from a group of dissimilar and independent to each other individual duration models are fed to a machine learning algorithm, which reconciles and fuses the outputs of the individual models, yielding more precise phone duration predictions. The performa...

Modeling Phone Duration of Lithuanian by Classification and Regression Trees, using Very Large Speech Corpus

Classification and regression tree approach was used in this research to model phone duration of Lithuanian. 300 thousand samples of vowels and 400 thousand samples of consonants extracted from VDU-AB20 corpus were used in experimental part of research. Set of 15 parameters characterizing phone and its context were selected for duration prediction. The most significant of them were: identifier ...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

Detection of Overlapping Speech in Meetings Using Support Vector Regression

A method of detecting overlapping speech in meetings is proposed in this paper. The eigenvalue distribution of the spatial correlation matrix reflects information on the relative power of sound sources. By applying Support Vector Regression to a set of input eigenvalues, the relative power of sources is estimated. Based on this, overlapping speech is then detected. The proposed method was evalu...

Phone duration modeling using gradient tree boosting