A Modified Speaking Rate Estimation Based on Frame-Level LSTM

Speaking rate has various applications in many domains such as speech recognition, speaker verification, emotion recognition, etc. It conveys long-term information in speech and changes over time which can be seen as a kind of time sequence. This paper proposes a frame-level LSTM speaking rate estimation method. Instead of taking the whole utterance as a sequence, the frame-level LSTM exploits the sequence information in each segment and brings a more precise segmented speaking rate estimation. We also evaluate the influence of fixed-length segmentation and voice activity detection(vad) segmentation on speaking rate estimation. Results show that the proposed frame-level LSTM method yields a high correlation between the estimated speaking rate and the ground truth. It achieves a relative improvement of 13.0% compared to the state of the art statistical learning method and 16.3% over the support vector regression(SVR) evaluated on the same TIMIT corpus.