STOP CONSONANT CLASSIFICTION USING RECURRANT NEURAL NETWORKS NSF Summer Undergraduate Fellowship in Sensor Technologies

This paper describes the use of recurrent neural networks for phoneme recognition. Spectral, Bark scaled, and cepstral representations for input to the networks are discussed, and an additional input based on algorithmically defined features is described that can also be used as input for phoneme recognition. Neural networks with recurrent hidden layers of various sizes are trained to determine, using the various input representations, whether a stop consonant is voiced or unvoiced, and whether the stop consonant is labial, alveolar, or palatal. For voicing detection the peak accuracy was 75% of the phonemes not used to train the network identified correctly, and for placement of articulation, the peak accuracy was 78.5% of the testing set identified correctly. Using the algorithmically defined features and a three-layer feedforward network, an average accuracy of 80% for voicing and 78% for placement of articulation. Implications of these results and further research needed are discussed.