Syllable nucleus Durations Estimation using Linear Regression based ensemble model

Unlike conventional automatic continuous speech segmentation models that deal with each boundary time-mark individually, in this paper, we propose an interval-data-based linear regression model for syllable nucleus durations estimation (LRM-DE), which treats syllable boundary time-marks in pairs. This characteristic of LRM-DE makes it more suitable for estimating syllable durations for English sentences, which can be used for sentence stress detection. LRM-DE combines the outcomes of multiple base automatic speech segmentation machines (ASMs) to generate final boundary time-marks that miminize the average distance of the predicted and reference boundary-pairs of syllable nuclei. Experimental results show that on TIMIT dataset, LRM-DE reduces the average difference between the predicted syllable nucleus durations and their reference ones from 13.64 ms (the best result of a single ASM) to 11.81 ms. Also, LRM-DE improves the syllable nucleus segmentation accuracy from 81.59% to 83.98% within a tolerance of 20 ms.