Darryl W. Purnell

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4ranked-venue papers

4as first author

0since 2021 · last 2002

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Speech recognition and synthesis · 91% Probabilistic and Bayesian machine learning · 9%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Natural language and speech › Speech recognition and synthesis acoustic model training	1	2002	Improved generalization of MCE parameter estimation with application to speech recognition · IEEE Trans. Speech Audio Process. 2002
Natural language and speech › Speech recognition and synthesis › acoustic model training discriminative training	1	2002	Improved generalization of MCE parameter estimation with application to speech recognition · IEEE Trans. Speech Audio Process. 2002
Natural language and speech › Speech recognition and synthesis › acoustic model training › discriminative training minimum classification error training	1	2002	Improved generalization of MCE parameter estimation with application to speech recognition · IEEE Trans. Speech Audio Process. 2002
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model hidden markov model	1	2002	Improved generalization of MCE parameter estimation with application to speech recognition · IEEE Trans. Speech Audio Process. 2002

Methods — techniques the papers use, named apart from their topics

minimum classification error training · 0.0generalization regularization · 0.0

Year	Publication	Venue	Position
2002	Improved generalization of MCE parameter estimation with application to speech recognition abstract Discriminative training of hidden Markov models (HMMs) using minimum classification error training (MCE) has been shown to work well for certain speech recognition applications. MCE is, however, somewhat prone to overspecialization. This study investigates various techniques which improve performance and generalization of the MCE algorithm. Improvements of up to 10% in relative error rate on the test set are achieved for the TIMIT dataset. Darryl W. Purnell, Elizabeth C. Botha	IEEE Trans. Speech Audio Process.	1
2001	Bayesian methods for HMM speech recognition with limited training data abstract This paper presents a Bayesian approach to learning for HMMs in speech recognition. The implementation of Bayesian learning for HMMs in speech recognition is discussed, including the requirement of maintaining the original HMM constraints, choice of prior and utterance recognition. This work shows that the Bayesian learning approach can be successfully applied to complex models when the amount of training data is small. Darryl W. Purnell, Elizabeth C. Botha	INTERSPEECH	1
2000	Improved performance and generalization of minimum classification error training for continuous speech recognition Darryl W. Purnell, Elizabeth C. Botha	INTERSPEECH	1
1998	Automatic face recognition in a heterogeneous population Darryl W. Purnell, Christoph Nieuwoudt, Elizabeth C. Botha	Pattern Recognit. Lett.	1