Title :
Robust voiced/unvoiced speech classification with self-organizing maps
Author_Institution :
Acoust. Lab., Helsinki Univ. of Technol., Espoo
fDate :
30 Apr-3 May 1995
Abstract :
The goal of this paper is to show the applicability of a new feature set in voiced/unvoiced (V/UV) classification of speech. The decision is based on the Kohonen-type Self-Organizing Maps (SOM) using this new feature set. The set of input features are computed according to the human auditory system using Warped Linear Prediction (WLP) and found to be robust to background noise - thus the classification is reliable for corrupted speech segments, too. Self-Organizing Maps classify noisy patterns with an error rate of less than 2% at 9 dB signal-to-noise ratio
Keywords :
pattern classification; prediction theory; self-organising feature maps; speech recognition; Kohonen-type self-organizing maps; corrupted segments; error rate; feature set; human auditory system; noisy patterns; robust voiced/unvoiced speech classification; signal-to-noise ratio; warped linear prediction; Acoustic noise; Auditory system; Filters; Frequency; Humans; Neural networks; Robustness; Self organizing feature maps; Speech coding; Speech synthesis;
Conference_Titel :
Circuits and Systems, 1995. ISCAS '95., 1995 IEEE International Symposium on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2570-2
DOI :
10.1109/ISCAS.1995.521423
