Demo Data from VLDB 2026 / reviewers in the wild / expert
Yan Han 0004
dblp:79/4311-4
· DBLP profile ↗
5ranked-venue papers
5as first author
0since 2021 · last 2007
0000-0001-9937-0981ORCID · corroborated
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 · 4 · 4 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 · 56% Probabilistic and Bayesian machine learning · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Speech recognition and synthesis
acoustic modeling |
0.1 | 1 | 2007 | Trajectory Clustering for Solving the Trajectory Folding Problem in Automatic Speech Recognition · IEEE Trans. Speech Audio Process. 2007 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model
hidden markov model |
0.1 | 1 | 2007 | Trajectory Clustering for Solving the Trajectory Folding Problem in Automatic Speech Recognition · IEEE Trans. Speech Audio Process. 2007 |
Natural language and speech › Speech recognition and synthesis › automatic speech recognition
continuous speech recognition |
0.0 | 1 | 2007 | Trajectory Clustering for Solving the Trajectory Folding Problem in Automatic Speech Recognition · IEEE Trans. Speech Audio Process. 2007 |
Methods — techniques the papers use, named apart from their topics
trajectory clustering · 0.1head-body-tail models · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Hierarchical acoustic modeling based on random-effects regression for automatic speech recognitionabstractContains fulltext : 44262.pdf (Publisher’s version ) (Closed access) Yan Han 0004, Lou Boves |
INTERSPEECH | 1 |
| 2007 | Trajectory Clustering for Solving the Trajectory Folding Problem in Automatic Speech RecognitionabstractIn this paper, we introduce a novel method for clustering speech gestures, represented as continuous trajectories in acoustic parameter space. Trajectory Clustering allows us to avoid the conditional independence assumption that makes it difficult to account for the fact that successive measurements of an articulatory gesture are correlated. We apply the trajectory clustering method for developing multiple parallel hidden Markov models (HMMs) for a continuous digits recognition task. We compare the performance obtained with data-driven clustering to the recognition performance obtained with conventional head-body-tail models, which use knowledge-based criteria for building multiple HMMs in order to obviate the trajectory folding problem. The results show that trajectory clustering is able to discover structure in the the training database that is different from the structure assumed by the knowledge-based approach. In addition, the data-derived structure gives rise to significantly better recognition performance, and results in a 10% word error rate reduction. Yan Han 0004, Johan de Veth, Lou Boves |
IEEE Trans. Speech Audio Process. | 1 |
| 2006 | Trajectory Clustering of Syllable-Length Acoustic Models for Continuous Speech RecognitionabstractRecent research suggests that modeling coarticulation in speech is more appropriate at the syllable level. However, due to a number of additional factors that affect the way syllables are articulated, creating multiple paths through syllable models might be necessary. Our previous research on longer-length multi-path models in connected digit recognition has proved trajectory clustering to be an attractive approach to deriving multi-path models. In this paper, we extend our research to large vocabulary continuous speech recognition by deriving trajectory clusters for 94 very frequent syllables in a 20-hour data set of Dutch read speech. The resulting clusters are compared with a knowledge-based classification. The comparison results suggest that multi-path models for syllables are difficult to build based on phonetic and linguistic knowledge. When multi-path models based on trajectory clustering are used, speech recognition performance improves significantly. Thus, it is concluded that data-driven trajectory clustering is a very effective approach to developing multi-path models Yan Han 0004, Annika Hämäläinen, Lou Boves |
ICASSP (1) | 1 |
| 2006 | Syllable-length path mixture hidden Markov models with trajectory clustering for continuous speech recognitionabstractItem does not contain fulltext Yan Han 0004, Lou Boves |
INTERSPEECH | 1 |
| 2005 | Speech trajectory clustering for improved speech recognitionabstractContains fulltext : 42030.pdf (Publisher’s version ) (Closed access) Yan Han 0004, Johan de Veth, Lou Boves |
INTERSPEECH | 1 |