eur op eanexhibitionofcreati vityandinnovation - Europe Direct Iasi

EUROINVENT 2013
32.3.
Speech segmentation method based on predictive neural
Title EN
networks - SROL_SegmRN
Authors Zbancioc Marius, Feraru Monica
Institute of Computer Science, Romanian Academy, Iasi
Institution
Brach
Patent no. ORDA Registration no. 3255/08.04.2013
The proposed method uses a predictive neural network for
speech segmentation of the vocal signal in order to extrac the
prosodic features, the Mel Frequency Cepstral coefficients -
MFCC, the Linear Predictive Cepstral coefficients - LPCC,
etc.. The user can change the overlapping of the analysis
windows, which affect the running times. During the
trainning of the neural network is displayed in percent the
stage of the file processing. The vocal signal applied to the
input network and the neural network prediction error is
graphically displayed with the boundaries of the interest
Description areas. It was applyed a median filter to "smooth" the
EN prediction error signal in order to easily extract the threshold
value used in segmentation. The methods included in our
current tool for determining the threshold of the
segmentation is based on clustering algorithm FCM (fuzzy c-
means), and the analysis of the prediction error histogram.
When the prediction error is small, the signal is in a vowels
areas and when the prediction error is high, the signal is
consonantic or is corresponding to the pause between
utterances. Only for the vowels areas will be extracted the
prosodic features or other features used in emotion
recognition, speaker identification, speech recognition etc.
Class no. 10. Information Technology and Communication
NATIONAL
200