Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638372
Title: The effects of additive noise in speaker recognition
Author: Openshaw, J. P.
Awarding Body: University College of Swansea
Current Institution: Swansea University
Date of Award: 1995
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Abstract:
This thesis is concerned with text independent speaker recognition and how the performance is affected should additive noise contaminate the speech. Initially, benchmark recognition results are obtained for MFCC, PLP and their Δ derivative features. The drastic effects of noise are clear: recognition errors increase from 3.4% to 60.5% if the test speech has an SNR of 15dB, a level not uncommon as a background office environment. Various attempts at compensating for the adverse effects of noise are investigated, such as explicit modelling, whereby the noise conditions expected to be found in the testing phase are included in the model. The performance is improved across a range of noise levels with this technique, although a-priori knowledge of the noise level is required when creating the models. A different technique, in essence a form of filtering, is used to map features extracted from noisy speech to match those with no noise. Both linear and non-linear transformation functions are investigated, with an artificial neural network, with its ability to model arbitrary functions, achieving the best performance. A reliance on a-priori knowledge of the noise level is still required when generating the transformation function. The technique of noise masking is found to give the features considerable insensitivity to additive noise. This is a simple technique which has little computational overheads, however, the optimum mask level is found to be dependent on the level of the additive noise, again implying a-priori knowledge of the noise level. A new feature is demonstrated in this thesis, the time-relative cepstral series, T-ReCS. The T-ReCS feature uses an estimate of the spectral change of the speech signal, which filters out any stationary spectral component.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.638372  DOI: Not available
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