the geometric distribution of states duration is one of the main performance limiting assumptions of hidden markov modeling of speech signals. stochastic segment models, generally, and segmental hmm, specifically, overcome this deficiency partly at the cost of more complexity in both training and recognition phases. in this paper, a new duration modeling approach is presented. the main idea of ...

Controlling timing in text-to-speech synthesis systems is complicated, because there are many contextual factors that affect timing; moreover, which factors matter and what their precise effects are varies among languages. We describe here a language-independent approach for duration control. At run time, a language-independent timing module accesses languagespecific tables. These tables specif...

In natural speech, durations of phonetic segments are strongly dependent on contextual factors. Quantitative descriptions of these contextual effects have appfications in text-to-speech synthesis and in automatic speech recognition. In this paper, we describe a speakerdependent system for predicting segmental duration from text, with emphasis on the statistical methods used for its construction...

Naturalness of synthetic speech highly depends on appropriate modeling of prosodic aspects. Mostly, three prosody components are modeled: segmental duration, pitch contour and intensity. In this study, we present our work on modeling segmental duration in Turkish using machinelearning algorithms, especially Classification and Regression Trees (CART). The models predict phone durations based on ...

Duration modeling is a fundamental task of prosody generation for Text To Speech (TTS) systems. The objective of this task is to predict the duration of a speech unit from its phonological representation. Duration modeling has a significant influence on the intelligibility and the naturalness of the synthesized speech. This paper presents a Neural Network (NN) based approach to predict the dura...