Phonetic Feature Discovery in Speech using Snap-Drift

This paper presents a new application of the snapdrift algorithm [1]: feature discovery and clustering of speech waveforms from nonstammering and stammering speakers. The learning algorithm is an unsupervised version of snapdrift which employs the complementary concepts of fast, minimalist learning (snap) & slow drift (towards the input pattern) learning. The SnapDrift Neural Network (SDNN) is toggled between snap and drift modes on successive epochs. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN

Feature discovery using snap-drift neural networks

This paper introduces an application of Snap-Drift Neural Networks (SDNNs), which employs the complementary concepts of fast, minimalist (snap) learning and slow (drift towards the input pattern) learning, for feature discovery and classification of speech waveforms from nonstammering and stammering speakers. The speech waveforms are drawn from a phonetically annotated corpus, which facilitates phonetic interpretation of the classes of patterns discovered by the SDNN. The results show that SDNN groups the phonetics speech input patterns meaningfully and extracts properties which are common to both non-stammering and stammering speech, as well as distinct features that are common within each of the utterance groups, thus supporting classification. SDNN is also being applied in a virtual learning environment to categorise students’ test responses and thereby support individualised feedback

Snap-Drift Neural Network for Selecting Student Feedback

This paper investigates the application of the snap- drift neural network (SDNN) to the provision of guided student learning in formative assessments. SDNN is able to adapt rapidly by performing a combination of fast, convergent, minimal intersection learning (snap) and Learning Vector Quantization (drift) to capture both precise sub-features in the data and more general holistic features. Snap and drift are combined within a modal learning system that toggles its learning style between the two modes. In this particular application the SDNN is trained with responses from past students to Multiple Choice Questions (MCQs). The neural network is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. The feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The trained snap-drift neural network is integrated into an on-line Multiple Choice Questions (MCQs) system. This approach has been implemented and trialled with two cohorts of students using data sets of student answers related to a topic from an Introduction to Computer System module. Results indicate that significant learning support is provided for the students

Diagnostic Feedback by Snap-drift Question Response Grouping

This work develops a method for incorporation into an on-line system to provide carefully targeted guidance and feedback to students. The student answers on-line multiple choice questions on a selected topic, and their responses are sent to a Snap-Drift neural network trained with responses from a past students. Snap-drift is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that category-group of students. In this way the feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The feedback is concept-based rather than tied to any particular question, and so the learner is encouraged to retake the same test and receives different feedback depending on their evolving state of knowledge

Combining neural modes of learning for handwritten digit recognition

An ADaptive Function Neural Network (ADFUNN) is combined with the on-line snapdrift learning method in this paper to perform optical and pen-based recognition of handwritten digits. Snap-Drift employs the complementary concepts of minimalist common feature learning (snap) and vector quantization (drift towards the input patterns), and is a fast unsupervised method suitable for real-time learning and non-stationary environments where new patterns are continually introduced. The ADaptive FUction Neural Network (ADFUNN) is based on a linear piecewise neuron activation function that is modified by a gradient descent supervised learning algorithm. It has previously been applied to the Iris dataset, and a natural language phrase recognition problem, exhibiting impressive generalisation classification ability without the hidden neurons that are usually required for linearly inseparable data. The unsupervised single layer Snap-Drift is effective in extracting distinct features from the complex cursive-letter datasets, and the supervised single layer ADFUNN is capable of solving linearly inseparable problems rapidly. In combination within one network (SADFUNN), these two methods are more powerful and yet simpler than MLPs (a standard neural network), at least on this problem domain. The optical and pen-based handwritten digits data are from UCI machine learning repository. The classifications are learned rapidly and produce higher generalisation results than a MLP with standard learning methods

Modal Learning Neural Networks

This paper will explore the integration of learning modes into a single neural network structure in which layers of neurons or individual neurons adopt different modes. There are several reasons to explore modal learning. One motivation is to overcome the inherent limitations of any given mode (for example some modes memorise specific features, others average across features, and both approaches may be relevant according to the circumstances); another is inspiration from neuroscience, cognitive science and human learning, where it is impossible to build a serious model without consideration of multiple modes; and a third reason is non-stationary input data, or time-variant learning objectives, where the required mode is a function of time. Two modal learning ideas are presented: The Snap-Drift Neural Network (SDNN) which toggles its learning between two modes, is incorporated into an on-line system to provide carefully targeted guidance and feedback to students; and an adaptive function neural network (ADFUNN), in which adaptation applies simultaneously to both the weights and the individual neuron activation functions. The combination of the two modal learning methods, in the form of Snap-drift ADaptive FUnction Neural Network (SADFUNN) is then applied to optical and pen-based recognition of handwritten digits with results that demonstrate the effectiveness of the approach

Question response grouping for online diagnostic feedback

This work develops a method for incorporation into an online system to provide carefully targeted guidance and feedback to students. The student answers online multiple choice questions on a selected topic, and their responses are sent to a SnapDrift neural network trained with responses from past students. Snapdrift is able to categorise the learner's responses as having a significant level of similarity with a subset of the students it has previously categorised. Each category is associated with feedback composed by the lecturer on the basis of the level of understanding and prevalent misconceptions of that categorygroup of students. In this way the feedback addresses the level of knowledge of the individual and guides them towards a greater understanding of particular concepts. The feedback is conceptbased rather than tied to any particular question, and so the learner is encouraged to retake the same test and receives different feedback depending on their evolving state of knowledge. This approach has been applied to two data sets related to topics from an Introduction to Computer System module and a Research Skills module

Continuous Reinforced Snap-Drift Learning in a Neural Architecture for Proxylet Selection in Active Computer Networks

A new continuous learning method is used to optimise the selection of services in response to user requests in an active computer network simulation environment. The learning is an enhanced version of the ‘snap-drift’ algorithm, which employs the complementary concepts of fast, minimalist (snap) learning and slower drift (towards the input patterns) learning, in a non-stationary environment where new patterns arrive continually. Snap is based on Adaptive Resonance Theory, and drift on Learning Vector Quantisation. The new algorithm swaps its learning style between these two self-organisational modes when declining performance is detected, but maintains the same learning mode during episodes of improved performance. Performance updates occur at the end of each epoch. Reinforcement is implemented by enabling learning on any given pattern with a probability that increases linearly with declining performance. This method, which is capable of rapid re-learning, is used in the design of a modular neural network system: Performance-guided Adaptive Resonance Theory (P-ART). Simulations involving a requirement to continuously adapt to make appropirate decisions within a BT active computer network environment, demonstrate the learning is stable, and able to discover alternative solutions in rapid response to new performance requirements or significant changes in the stream of input patterns

A Neural Network Approach for Intrusion Detection Systems

Intrusion detection systems, alongside firewalls and gateways, represent the first line of defense against computer network attacks. There are various commercial or open source intrusion detection systems in the market; nevertheless they do not perform well in various situations including novel attacks, user activity detection, generating in some cases false positive or negative alerts. The reason behind such performance is probably due to the implementation of merely signature based checks and a high degree of dependence on human interaction. On the other hand, a neural network approach might be the right one to tackle these issues. Neural networks have already been applied successfully to solve many problems related to pattern recognition, data mining, data compression and research is still underway with regards to intrusion detection systems. Unsupervised learning and fast network convergence are some features that can be integrated into an IDS system using neural networks. The networks can be designed to process a variety of data, although there are some constraints regarding input formatting. For this reason, data encoding represents a challenging task in the integration process since it needs to be optimised for the IDS domain. This paper will discuss the integration of IDS and neural networks, including data encoding and performance issues

Zulu phonology, tonology and tonal grammar.

Thesis (Ph.D.)-University of Natal, Durban, 1966.Zulu belongs to the Nguni group of the Southern Bantu languages, which are spoken throughout Southern Africa. Other groups are the Suthu and the Shona, which are spoken in the interior, whereas the Nguni languages are spoken towards the south-east coast, Xhosa in the eastern part of the Cape Province, Zulu in Natal and Zululand, and Swazi in Swaziland. Swazi represents a distinct variety of Nguni speech known as "tekela", characterized by t in place of Zulu and Xhosa z, ts or tf and dz or dv in place of Zulu and Xhosa t and d, and by other phonetical characteristics, but Zulu and Xhosa are so similar that they are linguistically dialects of the same language. However, they have important separate literatures and are generally regarded as separate languages. For these reasons and for the more real reason that it is in tonal structure that they differ most greatly, this study excludes Xhosa and concentrates on Zulu only