SUVING: AUTOMATIC SILENCE /UNVOICED/VOICED CLASSIFICATION OF SPEECH
Mark Greenwood, Andrew B. Kinghorn · 1999
This paper is concerned with labelling sections of speech samples based on whether they are silence, voiced or unvoiced speech. The labelling is done using calculations over the speech samples; zero crossing and short-term energy functions. These functions complement each other and as such can be used more accurately together to label the parts of speech. The results of applying these functions to ten speech samples are compared to the result of the same samples having been manually labelled, to produce a percentage accuracy for each speech file. This study found that the average percentage accuracy of the algorithms implemented, over all ten-speech samples was about 65%, and concludes that the accuracy could be slightly improved through the use of a more accurate windowing function.