A hierarchical model for syllable recognition

Inspired by recent findings on the similarities between the primary auditory and visual cortex we propose a neural network for speech recognition based on a hierarchical feedforward architecture for visual object recognition. When using a Gammatone filterbank for the spectral analysis the resulting spectrograms of syllables can be interpreted as images. After a preprocessing enhancing the formants in the speech signal and a length normalization, the images can than be fed into the visual hierarchy. We demonstrate the validity of our approach on the recognition of 25 different monosyllabic words and compare the results to the Sphinx-4 speech recognition system. Especially for noisy speech our hierarchical model achieves a clear improvement.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Xavier Domont, Martin Heckmann, Heiko Wersing, Frank Joublin, Christian Goerick
• Keywords: ESANN 2007, Hierarchical Feedforward Architecture, Neural Networks, Speech Recognition, Visual Object Recognition