Jon E. M. Tyler

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dblp:21/11422 · DBLP profile ↗

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1ranked-venue papers

0as first author

0since 2021 · last 1994

—ORCID · none

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

Artificial intelligence and machine learning · 1

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 · 50% Video understanding and tracking · 38% Probabilistic and Bayesian machine learning · 12%

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 › automatic speech recognition isolated word recognition	1	1994	Stochastic modeling of temporal information in speech for hidden Markov models · IEEE Trans. Speech Audio Process. 1994
Computer vision › Video understanding and tracking temporal modeling	1	1994	Stochastic modeling of temporal information in speech for hidden Markov models · IEEE Trans. Speech Audio Process. 1994
Natural language and speech › Speech recognition and synthesis acoustic modeling	1	1994	Stochastic modeling of temporal information in speech for hidden Markov models · IEEE Trans. Speech Audio Process. 1994
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model hidden markov model	1	1994	Stochastic modeling of temporal information in speech for hidden Markov models · IEEE Trans. Speech Audio Process. 1994

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

markov chain · 0.0hidden markov model · 0.0

Year	Publication	Venue	Position
1994	Stochastic modeling of temporal information in speech for hidden Markov models abstract A Markov chain, namely, the temporal Markov model, is used to model the time-ordering information of the feature vectors of a spoken word. An empirical method is suggested to combine the temporal Markov model (TMM) with the hidden Markov model (HMM) for word recognition. Experiments on speaker-independent isolated English alphabet recognition showed that this method is effective in terms of improved recognition.> Jianing Dai, Iain G. MacKenzie, Jon E. M. Tyler	IEEE Trans. Speech Audio Process.	3