Catia Cucchiarini

TL;DR: This contribution provides an overview of the publications on pronunciation variation modeling in automatic speech recognition, paying particular attention to the papers in this special issue and the papers presented at 'the Rolduc workshop'.

TL;DR: This paper examines the relationship between pedagogy and technology in Computer Assisted Pronunciation Training (CAPT) courseware and shows that many commercial systems tend to prefer technological novelties to the detriment of pedagogical criteria that could benefit the learner more.

TL;DR: Rate of speech appears to be the best predictor of perceived fluency: for six automatic measures the magnitude of the correlations with the fluency scores varies between 0.81 and 0.93, and two other important determinants of reading fluency are the rate at which speakers articulate the sounds and the number of pauses they make.

TL;DR: The results show that the objective measures investigated in this study can be employed to predict fluency ratings, but the predictive power of such measures is stronger for read speech than for spontaneous speech.

TL;DR: This research investigates pronunciation errors frequently made by foreigners learning Dutch as a second language and compares four types of classifiers that can be used to detect erroneous pronunciations of these phones.