Browsing by Author "Niesler, Thomas"
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- ItemAnimal-borne behaviour classification for sheep (Dohne Merino) and rhinoceros (Ceratotherium simum and diceros bicornis)(BioMed Central, 2017-11-21) Le Roux, Solomon Petrus; Marias, Jacques; Wolhuter, Riaan; Niesler, ThomasBackground: The ability to study animal behaviour is important in many fields of science, including biology, behavioural ecology and conservation. Behavioural information is usually obtained by attaching an electronic tag to the animal and later retrieving it to download the measured data. We present an animal-borne behaviour classification system, which captures and automatically classifies three-dimensional accelerometer data in real time. All computations occur on specially designed biotelemetry tags while attached to the animal. This allows the probable behaviour to be transmitted continuously, thereby providing an enhanced level of detail and immediacy. Results: The performance of the animal-borne automatic behaviour classification system is presented for sheep and rhinoceros. For sheep, a classification accuracy of 82.40% is achieved among five behavioural classes (standing, walking, grazing, running and lying down). For rhinoceros, an accuracy of 96.10% is achieved among three behavioural classes (standing, walking and lying down). The estimated behaviour was established approximately every 5.3 s for sheep and 6.5 s for rhinoceros. Conclusions: We demonstrate that accurate on-animal real-time behaviour classification is possible by successful design, implementation and deployed on sheep and rhinoceros. Since the bandwidth required to transmit the behaviour class is lower than that which would be required to transmit the accelerometer measurements themselves, this system is better suited to low-power and error-prone data communication channels that may be expected in the animals habitat.
- ItemAutomatic discovery of subword units and pronunciations for automatic speech recognition using TIMIT(PRASA, 2010-11) Goussard, George; Niesler, ThomasWe address the automatic generation of acoustic subword units and an associated pronunciation dictionary for speech recognition. The speech audio is first segmented into phoneme-like units by detecting points at which the spectral characteristics of the signal change abruptly. These audio segments are subsequently subjected to agglomerative clustering in order to group similar acoustic segments. Finally, the orthography is iteratively aligned with the resulting transcription in terms of audio clusters in order to determine pronunciations of the training words. The approach is evaluated by applying it to two subsets of the TIMIT corpus, both of which have a closed vocabulary. It is found that, when vocabulary words occur often in the training set, the proposed technique delivers performance that is close to but lower than a system based on the TIMIT phonetic transcriptions. When vocabulary words are not repeated often in the training set, the best system is able to outperform its counterpart based on the TIMIT phonetic transcriptions, although recognition performance in both cases is poor.
- ItemClustering acoustic segments using multi- stage agglomerative hierarchical clustering(Public Library of Science, 2015) Lerato, Lerato; Niesler, ThomasAgglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy. The data is first split into independent subsets, each of which is clustered separately. Thus reduces the storage required for sequential implementations, and allows concurrent computation on parallel computing hardware. The resultant clusters are merged and subsequently re-divided into subsets, which are passed to the following iteration. We show that MAHC can match and even surpass the performance of the exact implementation when applied to datasets of speech segments.
- ItemA whistle-stop tour of automatic speech recognition(Stellenbosch : Stellenbosch University, 2013-11) Niesler, ThomasThomas Niesler’s academic career began at the University of Stellenbosch where he obtained the BEng and MEng degrees in Electronic Engineering in 1991 and 1993 respectively. He moved to St John’s College, Cambridge, in 1994 as a Benefactor’s Scholar and obtained his PhD from the University of Cambridge in 1998 on the subject of statistical language modelling for large vocabulary speech recognition. He was employed first as a research associate (1997) and subsequently as a lecturer (1998–2000) by the University of Cambridge, where he taught undergraduate and master’s-level courses as part of the MPhil in Computer Speech and Language Processing. He joined the Department of Electrical and Electronic Engineering at the University of Stellenbosch in November 2000, where he teaches undergraduate and postgraduate students in signal and speech processing. In 2002 he was an invited researcher at the NTT Speech Open Laboratory in Kyoto, Japan. He has authored 22 journal publications, as well as 24 refereed international and a further 19 national conference publications. He holds a C2 rating with the South African National Research Foundation, and has supervised 16 postgraduate students to graduation. His research interests lie in signal and pattern recognition, with a particular emphasis on human language technology. He is a member of the International Speech Communication Association.