Whispery Speech Recognition using Adapted Articulatory Features
Szu-Chen Stan Jou, Tanja Schultz, Alex Waibel · 2006
This paper describes our research on adaptation methods applied to articulatory feature detection on soft whispery speech recorded with a throat microphone. Since the amount of adaptation data is small and the testing data is very different from the training data, a series of adaptation methods is necessary. The adaptation methods include: maximum likelihood linear regression, feature-space adaptation, and re-training with downsampling, sigmoidal low-pass filter, and linear multivariate regression. Adapted articulatory feature detectors are used in parallel to standard senone-based HMM models in a stream architecture for decoding. With these adaptation methods, articulatory feature detection accuracy improves from 87.82% to 90.52% with corresponding F-measure from 0.504 to 0.617, while the final word error rate improves from 33.8% to 31.2%.