Speech Recognition Using Connectionist Networks Dissertation Proposal

The thesis of the proposed research is that connectionist networks are adequate models for the problem of acoustic phonetic speech recognition by computer. Adequacy is defined as suitably high recognition performance on a representative set of speech recognition problems. Seven acoustic phonetic problems are selected and discussed in relation to a physiological theory of phonetics. It is argued that the selected tasks are sufficiently representative and difficult to constitute a reasonable test of adequacy. A connectionist network is a fine-grained parallel distributed processing configuration, in which simple processing elements are interconnected by scalar links. A connectionist network model for speech recognition has been defined called the temporal flow model. The model incorporates link propagation delay and internal feedback to express temporal relationships. The model is contrasted with other connectionist models in which time is represented explicitly by separate processing elements for each time sample. It has been shown previously that temporal flow models can be \u27trained\u27 to perform successfully some speech recognition tasks. A method of \u27learning\u27 using techniques of numerical nonlinear optimization has been demonstrated. Methods for extending these results to the problems selected for this research are presented

GRADSIM: A Connectionist Network Simulator Using Gradient Optimization Techniques

A simulator for connectionist networks which uses gradient methods of nonlinear optimization for network learning is described. The simulator (GRADSIM) was designed for temporal flow model connectionist networks. The complete gradient is computed for networks of general connectivity, including recurrent links. The simulator is written in C, uses simple network and data descriptors for flexibility, and is easily modified for new applications. A version of the simulator which precompiles the network objective function and gradient computations for greatly increased processing speed is also described. Benchmark results for the simulator running on the DEC VAX 8650, SUN 3/260 and CYBER 205 are presented