Modeling syllable duration in Indian languages using neural networks

We propose a neural network model for predicting the syllable duration in Indian languages. A four layer feedforward neural network trained with a backpropagation algorithm is used for modeling the syllable duration. Analysis is performed on broadcast news data in Hindi, Telugu and Tamil in order to predict the duration of syllables in these languages using a neural network model. The input to the neural network consists of a set of phonological, positional and contextual features extracted from the text. About 88% of the syllable durations are predicted within 25% of the actual duration. The relative importance of the positional and contextual features are examined separately.