Normal rate to fast rate speech conversion using non linear compression maps
This paper presents a new technique to convert normal rate speech into intelligible fast rate, speeded speech. Speeded speech has long been recognized for its potential to improve spoken media comprehension; however, current tools to significantly speed playback of non text media are insufficient due to their reliance on inaccurate phoneme analysis. With the ever increasing amount of non text media online, a method to speed playback that is agnostic of phonemes is needed. Our technique uses spectral and source components of the acoustics to generate a non linear compression map that characterizes how conversational rate speech signals are compressed to achieve analogue fast rate speech signals. A data set containing conversational and fast rate speech pairs was processed to determine compression maps corresponding to each pair. A Recursive Neural Network (RNN) was trained on the set of normal rate speech and the corresponding compression maps. The RNN was then used to generate compression maps for novel