Self-Organizing Grammar Induction Using a Neural Network Model

This paper presents a self-organizing, real-time, hierarchical neural network model of sequential processing, and shows how it can be used to induce recognition codes corresponding to word categories and elementary grammatical structures. The model, first introduced in Mannes (1992), learns to recognize, store, and recall sequences of unitized patterns in a stable manner, either using short-term memory alone, or using long-term memory weights. Memory capacity is only limited by the number of nodes provided. Sequences are mapped to unitized patterns, making the model suitable for hierarchical operation. By using multiple modules arranged in a hierarchy and a simple mapping between output of lower levels and the input of higher levels, the induction of codes representing word category and simple phrase structures is an emergent property of the model. Simulation results are reported to illustrate this behavior.National Science Foundation (IRI-9024877

A Neural Network Model of Spatio-Temporal Pattern Recognition, Recall and Timing

This paper describes the design of a self~organizing, hierarchical neural network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall {both from STM and from LTM) is performed with a learned rhythmical structure. The network, bearing similarities with ART (Carpenter & Grossberg 1987a), learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operation. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.National Science Foundation (IRI-9024877

The use of neural networks to characterise problematic arc sounds

Automation of electric arc welding has been at the centre of considerable debate and the subject of much research for several decades. One conclusion drawn from all this effort is that there seems to be no single system that can monitor all of the variables and subsequently, fully control any welding process. To date there has been considerable success in the development of seam tracking systems employing various sensing techniques, good progress has been made in the area of penetration measurement and worthwhile use has been made of the integration of expert systems and modelling software within these control domains. Skilled welders develop their own monitoring and control systems and it has been observed that part of this expertise is the ability to listen subconsciously to the sound of the arc and to alter the electrode position in response to an adverse change in arc noise. Attempts have been made to analyse these sounds using both conventional techniques and more recently expert systems, neither have delivered any usable information. This paper describes a new approach involving the use of neural networks in the identification of sounds which indicate that the welding system is drifting out of control

Regional cerebral blood flow correlates of orthographic analysis and phonetic discrimination in adults who were reading disabled children

This research study was undertaken to test a model of posterior displacement of focal perisylvian activation based on the autopsy work of Galaburda, the intraoperative work of Ojemann and Rasmussen, and neural developmental animal work. By this model, an early lesion in Wernicke's area would displace some aspects of the neuronal processing capacity originally destined for Wernicke's area to adjacent, posterior cites. Forty-one normal adult males and 47 adult males with documented childhood reading evaluations (the Orton group) performed an orthographic analysis (spelling) task. Some also did phonetic and tonal tasks. Regional cerebral blood flow was measured during task performance using the 133-Xenon inhalation method. Normal subjects showed cerebral activation at left Wernicke's area proportional to spelling task accuracy, while Orton subjects showed activation both at Wernicke's area and at the left angular gyrus such that better performers of the spelling task activated Wernicke's area more and angular gyrus less. An inverse relationship between childhood reading impairment and angular gyrus activity also was found, and this was independent of either task accuracy or adult reading attainment

The management of industrial arc welding by neural networks

New methods of monitoring industrial process variables are constantly being sought with the aim to improve control efficiency. It has been observed that skilled welders subconsciously adapt their manual arc welding technique in response to a variation in the sound produced from the process. This paper proposes an approach to the control of an automated submerged arc welding process using:- 1. Real time monitoring of acoustic emissions 2. The application of neural networks to predict the point of instability of the process variables

Are words easier to learn from infant- than adult-directed speech? A quantitative corpus-based investigation

We investigate whether infant-directed speech (IDS) could facilitate word form learning when compared to adult-directed speech (ADS). To study this, we examine the distribution of word forms at two levels, acoustic and phonological, using a large database of spontaneous speech in Japanese. At the acoustic level we show that, as has been documented before for phonemes, the realizations of words are more variable and less discriminable in IDS than in ADS. At the phonological level, we find an effect in the opposite direction: the IDS lexicon contains more distinctive words (such as onomatopoeias) than the ADS counterpart. Combining the acoustic and phonological metrics together in a global discriminability score reveals that the bigger separation of lexical categories in the phonological space does not compensate for the opposite effect observed at the acoustic level. As a result, IDS word forms are still globally less discriminable than ADS word forms, even though the effect is numerically small. We discuss the implication of these findings for the view that the functional role of IDS is to improve language learnability.Comment: Draf

Singing voice resynthesis using concatenative-based techniques

Tese de Doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201

Embedding speech into virtual realities

In this work a speaker-independent speech recognition system is presented, which is suitable for implementation in Virtual Reality applications. The use of an artificial neural network in connection with a special compression of the acoustic input leads to a system, which is robust, fast, easy to use and needs no additional hardware, beside a common VR-equipment