Local Feature Extractors Accelerating HNNP for Phoneme Recognition

Artificial neural networks are fast in the application phase but very slow in the training phase. On the other hand there are state-of-the-art approaches using neural networks, which are very efficient in image classification tasks, like the hybrid neural network plait (HNNP) approach for images from signal data stemming for instance from phonemes. We propose to accelerate HNNP for phoneme recognition by substituting the neural network with the highest computation costs, the convolutional neural network, within the HNNP by a preceding local feature extractor and a simpler and faster neural network. Hence, in this paper we propose appropriate feature extractors for this problem and investigate and compare the resulting computation costs as well as the classification performance. The results of our experiments show that HNNP with the best one of our proposed feature extractors in combination with a smaller neural network is more than two times faster than HNNP with the more complex convolutional neural network and delivers still a good classification performance.