Speech Processing by Computer
By the end of the session you should:
q be able to describe three applications of neural networks in speech and language
q have a sense of how neural network models may be used to process language data, and how they may be trained and tested
q be able to describe some strengths and weaknesses of the three applications, particularly with reference to symbolic alternatives
1. Learning past-tense morphology
q Generating the phonological representation of the past-tense form of given verb roots
q Learned from examples
q Tested for generalisation ability
2. Lexical access
q Identifying words from phonetic transcription
q Lexical effect, lexical segmentation, time course of lexical access
3. Phoneme recognition
q Identifying phoneme probabilities from acoustic input
q Learned from large annotated speech corpus
q Generalisation abilities of networks
q Predictive power of networks as cognitive model
q Practical model of some cognitive processes
q “Connectionism”, Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/entries/connectionism/
D.E.Rumelhart & J.L.McClelland, On learning the past tenses of English verbs, in Parallel Distributed Processing, Vol 2, Chapter 18, MIT Press 1986, pp216-271.
J.L. McClelland & J.L. Elman , Interactive processes in speech perception: the TRACE model, in Parallel Distributed Processing, Vol 2, Chapter 15, MIT Press 1986, pp58-121.
A.J. Robinson, An application of recurrent nets to phone probability estimation, IEEE Transactions on neural networks, 5 (1994), pp298-305.