Technical Program

Paper Detail

Paper:SP-P6.10
Session:Feature Analysis for ASR, TTS, and Verification
Time:Wednesday, May 19, 09:30 - 11:30
Presentation: Poster
Topic: Speech Processing: Feature Extraction
Title: ON USE OF TASK INDEPENDENT TRAINING DATA IN TANDEM FEATURE EXTRACTION
Authors: Sunil Sivadas; Oregon Health & Science University, United States / IDIAP 
 Hynek Hermansky; IDIAP 
Abstract: The problem we address in this paper is, whether the feature extraction module trained on large amounts of task independent data, can improve the performance of stochastic models? We show that when there is only a small amount of task specific training data available, tandem features trained on task independent data give considerable improvement over Perceptual Linear Prediction (PLP) cepstral features in Hidden Markov Model (HMM) based speech recognition systems.
 
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