Analyzing training dependencies and posterior fusion in discriminant classification of apnoea patients based on sustained and connected speech

We present a novel approach using both sustained vowels and connected speech, to detect obstructive sleep apnea (OSA) cases within a homogeneous group of speakers. The proposed scheme is based on state-of-the-art GMM-based classifiers, and acknowledges specifically the way in which acoustic models are trained on standard databases, as well as the complexity of the resulting models and their adaptation to specific data. Our experimental database contains a suitable number of utterances and sustained speech from healthy (i.e control) and OSA Spanish speakers. Finally, a 25.1% relative reduction in classification error is achieved when fusing continuous and sustained speech classifiers. Index Terms: obstructive sleep apnea (OSA), gaussian mixture models (GMMs), background model (BM), classifier fusion

Multivariate Cepstral Feature Compensation on Band-limited Data for Robust Speech Recognition

Proceedings of the 16th Nordic Conference of Computational Linguistics NODALIDA-2007. Editors: Joakim Nivre, Heiki-Jaan Kaalep, Kadri Muischnek and Mare Koit. University of Tartu, Tartu, 2007. ISBN 978-9985-4-0513-0 (online) ISBN 978-9985-4-0514-7 (CD-ROM) pp. 144-151

Statistical class-based MFCC enhancement of filtered and band-limited speech for robust ASR

Proceedings of Interspeech-Eurospeech 2005, Lisbon (Portugal)In this paper we address the problem of bandwidth extension from the point of view of ASR. We show that an HMM-based recognition engine trained with full-bandwidth data can successfully perform ASR on limited-bandwidth test data by means of a simple correction scheme over the input feature vectors. In particular we show that results obtained using full-bandwidth HMMs and corrected feature vectors can be comparable to, or even outperform results obtained using limited-bandwidth-trained HMMs. Both results are inferior to those obtained with full-bandwidth HMMs and test data. These results suggest that the effect of channel mismatch on recognition accuracy can be partially compensated with a feature correction scheme, while the loss of information inherent to a limited-bandwidth cannot be compensated

Robust speech recognition under band-limited channels and other channel distortions

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, junio de 200