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Jianing Dai

dblp:44/1868 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 1995
—ORCID · none

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

Artificial intelligence and machine learning · 3 · 3 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
2 papers
Speech recognition and synthesis · 50% Probabilistic and Bayesian machine learning · 32% Video understanding and tracking · 18%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis › automatic speech recognition
isolated word recognition		 0.021995
Isolated word recognition using Markov chain models · IEEE Trans. Speech Audio Process. 1995
Stochastic modeling of temporal information in speech for hidden Markov models · IEEE Trans. Speech Audio Process. 1994
Machine learning › Probabilistic and Bayesian machine learning › stochastic processes
markov chain		 0.011995
Isolated word recognition using Markov chain models · IEEE Trans. Speech Audio Process. 1995
Computer vision › Video understanding and tracking
temporal modeling		 0.011994
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		 0.021995
Isolated word recognition using Markov chain models · IEEE Trans. Speech Audio Process. 1995
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		 0.021995
Isolated word recognition using Markov chain models · IEEE Trans. Speech Audio Process. 1995
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
YearPublicationVenuePosition
1995 Robust estimation of HMM parameters using fuzzy vector quantization and Parzen's window
Jianing Dai
 Pattern Recognit.1
1995 Isolated word recognition using Markov chain models
abstract
The paper describes how Markov chains may be applied to speech recognition. In this application, a spectral vector is modeled by a state of the Markov chain, and an utterance is represented by a sequence of states. The Markov chain model (MCM) offers a substantial reduction in computation, but at the expense of a significant increase in memory requirement when compared to the hidden Markov model (HMM). Experiments on isolated word recognition show that the MCM achieved results that are comparable to those of the HMMs tested for comparison.
Jianing Dai
 IEEE Trans. Speech Audio Process.1
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.1