Showing papers by "Richard P. Lippmann published in 1991"
TL;DR: Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities.
Abstract: Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, sim...
1,140 citations
14 Apr 1991
TL;DR: The techniques and experiments described are the first demonstration of a complete system that accepts speech messages as input and produces as estimated message class as output and demonstrate the feasibility of the technology and illustrate the need for further work.
Abstract: The components of a speech message information retrieval system include an acoustic front end which provides an incomplete transcription of a spoken message, and a message classifier that interprets the incomplete transcription and classifies the message according to message category. The techniques and experiments described are concerned with the integration of these components and represent the first demonstration of a complete system that accepts speech messages as input and produces as estimated message class as output. The complete system has been implemented on special-purpose digital signal processing hardware and demonstrated using live speech input. The results obtained on a conversational speech task have demonstrated the feasibility of the technology and also illustrate the need for further work. Even with a perfect acoustic front end, a message classification accuracy of only 78% was obtained with a 126 keyword vocabulary. >
57 citations
Proceedings Article•
02 Dec 1991TL;DR: It is demonstrated that RBF networks can be successfully incorporated in hybrid recognizers and suggested that they may be capable of good performance with fewer parameters than required by Gaussian mixture classifiers.
Abstract: A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid system was based. These results and additional experiments demonstrate that RBF networks can be successfully incorporated in hybrid recognizers and suggest that they may be capable of good performance with fewer parameters than required by Gaussian mixture classifiers. A global parameter optimization method designed to minimize the overall word error rather than the frame recognition error failed to reduce the error rate.
6 citations