Castro Martínez, Angel Mario and Meyer, Bernd T. (2015) Mutual benefits of auditory spectro-temporal Gabor features and deep learning for the 3rd CHiME Challenge. In: CHiME Challenge. (Unpublished)

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In this study, we combine our feature extraction scheme inspired by the human auditory system with a deep learning approach in the context of the 3rd CHiME Speech Separation and Recognition Challenge. The use of spectro-temporal Gabor features is motivated by physiological measurements in the primary auditory cortex that indicated neurons to be sensitive to localized, spectro-temporal patterns of an acoustic stimulus. We exploit such information by replacing the baseline filter bank features with features from a two-dimensional convolution of the log-Mel-spectrogram and the proposed Gabor filter bank. Together with some other minor parameter adjustments, we obtained relative improvements over the real data of 26% for the development set, and 34.24% for the evaluation set compared to the CHiME 3 baseline.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: speech recognition, auditory features, deep learning, discriminative training
Subjects: Generalities, computers, information > Computer science, internet
Language > English
Science and mathematics > Physics
Divisions: Faculty of Medicine and Health Sciences > Department of Medical Physics and Acoustics
Date Deposited: 24 Sep 2015 12:39
Last Modified: 24 Sep 2015 12:39
URN: urn:nbn:de:gbv:715-oops-25908

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