Abstract
This paper proposes a stress detection method using word-length dependent classifiers. Most of the past studies focused on finding the stress position of a word without looking into the length of that word. However, in a CAPT (computer-assisted pronunciation training) scenario, the prompted word for the students is known in advance, and we can make use of this extra information to greatly improve the detection accuracy. In the proposed method, a Bayesian classifier based on GMMs (Gaussian mixture models) is trained for words of each word-length. The experimental result shows that the proposed method improves upon the existing stress detection methods. A comprehensive dataset for stress detection is also released, and this dataset, to the best knowledge of authors, is the first publicly released stress detection dataset in the community.
