Tonotopic multi-layered perceptron: a neural network for learning long-term temporal features for speech recognition

We have been reducing word error rates (WER) on conversational telephone speech (CTS) tasks by capturing long-term (/spl sim/500ms) temporal information using multilayered perceptrons (MLP). In this paper we experiment with an MLP architecture called tonotopic MLP (TMLP), incorporating two hidden layers. The first of these is tonotopically organized: for each critical band, there is a disjoint set of hidden units that use the long-term energy trajectory as the input. Thus, each of these subsets of hidden units learns to discriminate single band energy trajectory patterns. The rest of the layers are fully connected to their inputs. When used in combination with an intermediate-term (/spl sim/100ms) MLP system to augment standard PLP features, the TMLP reduces the WER on the 2001 Nist Hub-5 CTS evaluation set (Eval2001) by 8.87% relative. We show some practical advantages over our previous methods. We also report results from a series of experiments to determine the best ranges of hidden layer sizes and total parameters with respect to the number of training patterns for this task and architecture.