Automatic Fluency Evaluation of Spontaneous Speech Using Disfluency Based Features
This paper describes an automatic fluency evaluation of spontaneous speech. Although we regularly observe a variety of different disfluencies in spontaneous speech, we focus on two types of phenomena, i.e. filled pauses and word fragments. This paper aims to reveal that these two types of disfluencies have effects on speech fluency evaluation differently. To this end, we conduct a series of SVM classification experiments on the Japanese spontaneous speech corpus. The experimental results show that the features derived from word fragments are effective in evaluating disfluent speech especially when combined with prosodic features such as speech rate and pauses/silence, while the features from filled pauses are not effective in evaluating fluency.
ICASSP 2020 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)