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Now showing items 21-30 of 61
Effects of application type on the choice of interaction modality in IVR systems
(Unisa Press, 2012)
This paper addresses the feasibility of using the telephone as a tool for information access in the technology challenged and illiterate communities of Southern Africa. We did two case studies of disparate Interactive Voice ...
A smartphone-based ASR data collection tool for under-resourced languages
(Elsevier, 2014)
Acoustic data collection for automatic speech recognition (ASR) purposes is a particularly challenging task when working with under-resourced languages, many of which are found in the developing world. We provide a brief ...
Classifying recognised speech with deep neural networks
(Southern African Conference for Artificial Intelligence Research, 2020)
We investigate whether word embeddings using deep neural networks can assist in the analysis of text produced by a speechrecognition system. In particular, we develop algorithms to identify which words are incorrectly ...
Optimising word embeddings for recognised multilingual speech
(Southern African Conference for Artificial Intelligence Research, 2020)
Word embeddings are widely used in natural language processing (NLP) tasks. Most work on word embeddings focuses on monolingual languages with large available datasets. For embeddings to be useful in a multilingual ...
Benign interpolation of noise in deep learning
(South African Institute of Computer Scientists and Information Technologists, 2020)
The understanding of generalisation in machine learning is in a state of flux, in part due to the ability of deep learning models to interpolate noisy training data and still perform appropriately on out-of-sample data, ...
Using a meta-model to compensate for training-evaluation mismatches
(Southern African Conference for Artificial Intelligence Research, 2020)
One of the fundamental assumptions of machine learning is
that learnt models are applied to data that is identically distributed to
the training data. This assumption is often not realistic: for example,
data collected ...
Insights regarding overfitting on noise in deep learning
(In Proc. South African Forum for Artificial Intelligence Research (FAIR2019), 2019-12)
The understanding of generalization in machine learning is in a state of flux. This is partly due to the relatively recent revelation that deep learning models are able to completely memorize training data and still perform ...
ReLU and sigmoidal activation functions
(In Proc. South African Forum for Artificial Intelligence Research (FAIR2019), 2019-12)
The generalization capabilities of deep neural networks are not well understood, and in particular, the influence of activation functions on generalization has received little theoretical attention. Phenomena such as ...
DNNs as layers of cooperating classifiers
(AAAI Press, 2020-01)
A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive. Classical attempts have produced complexity metrics that rely ...
Collecting and evaluating speech recognition corpora for 11 South African languages
(Springer, 2011)
We describe the Lwazi corpus for automatic speech recognition (ASR), a new telephone speech corpus which contains data from the eleven official languages of South Africa. Because of practical constraints, the amount of ...