Detecting Filled Pauses and Lengthenings in Russian Spontaneous Speech Using SVM

Spontaneous speech differs from any other type of speech in many ways. And the presence of speech disfluencies is its prominent characteristic. These phenomena are important feature in human-human communication and at the same time a challenging obstacle for the speech processing tasks. This paper reports the experiment results on automatic detection of filled pauses and sound lengthenings basing on the automatically extracted acoustic features. We have performed machine learning experiments using support vector machine (SVM) classifier on the mixed and quality diverse corpus of Russian spontaneous speech. We applied Gaussian filtering and morphological opening to post-process the probability estimates from an SVM classifier. As the result we achieved F1–score of 0.54, with precision and recall being 0.55 and 0.53 respectively.

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