ISCA Archive Interspeech 2008
ISCA Archive Interspeech 2008

Phoneme recognition based on hybrid neural networks with inhibition/enhancement of distinctive phonetic feature (DPF) trajectories

Mohammad Nurul Huda, Kouichi Katsurada, Tsuneo Nitta

In this paper, we introduce a novel distinctive phonetic feature (DPF) extraction method that incorporates inhibition/enhancement functionalities by discriminating the DPF dynamic patterns of trajectories relevant or not. The trajectories of each DPF show a convex pattern when the DPF is relevant and a concave one when irrelevant. The proposed algorithm enhances convex type patterns and inhibits concave type patterns. We implement the algorithm into a phoneme recognizer and evaluate it. The recognizer consists of two stages. The first stage extracts 45 dimensional DPF vectors from local features (LFs) of input speech using a hybrid neural network and incorporates an inhibition/enhancement network to obtain modified DPF patterns, and the second stage orthogonalizes the DPF vectors and then feeds them to an HMM-based classifier. The proposed phoneme recognizer significantly improves the phoneme recognition accuracy with fewer mixture components by resolving coarticulation effects.


doi: 10.21437/Interspeech.2008-438

Cite as: Huda, M.N., Katsurada, K., Nitta, T. (2008) Phoneme recognition based on hybrid neural networks with inhibition/enhancement of distinctive phonetic feature (DPF) trajectories. Proc. Interspeech 2008, 1529-1532, doi: 10.21437/Interspeech.2008-438

@inproceedings{huda08_interspeech,
  author={Mohammad Nurul Huda and Kouichi Katsurada and Tsuneo Nitta},
  title={{Phoneme recognition based on hybrid neural networks with inhibition/enhancement of distinctive phonetic feature (DPF) trajectories}},
  year=2008,
  booktitle={Proc. Interspeech 2008},
  pages={1529--1532},
  doi={10.21437/Interspeech.2008-438}
}