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Kaiyang Lin

dblp:245/3262 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · conflict

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

Artificial intelligence and machine learning · 2 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 · 61% Language models and text generation · 39%

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

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis
automatic speech recognition		 0.412019
Location-Based End-to-End Speech Recognition with Multiple Language Models · AAAI 2019
Natural language and speech › Speech recognition and synthesis › automatic speech recognition
end-to-end speech recognition		 0.412019
Location-Based End-to-End Speech Recognition with Multiple Language Models · AAAI 2019
Natural language and speech › Language models and text generation
language model integration		 0.412019
Location-Based End-to-End Speech Recognition with Multiple Language Models · AAAI 2019
Natural language and speech › Language models and text generation › text correction
semantic error correction		 0.112019
Location-Based End-to-End Speech Recognition with Multiple Language Models · AAAI 2019

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

multi-language-model decoding · 0.4end-to-end deep learning · 0.4
YearPublicationVenuePosition
2025 Removing multi-path echoes in underwater 3D reconstruction via multi-view consistency
Binyu Nie, Yunxuan Feng, Haowen Gao, Kaiyang Lin
 Pattern Recognit. Lett.5
2019 Location-Based End-to-End Speech Recognition with Multiple Language Models
abstract
End-to-End deep learning approaches for Automatic Speech Recognition (ASR) has been a new trend. In those approaches, starting active in many areas, language model can be considered as an important and effective method for semantic error correction. Many existing systems use one language model. In this paper, however, multiple language models (LMs) are applied into decoding. One LM is used for selecting appropriate answers and others, considering both context and grammar, for further decision. Experiment on a general location-based dataset show the effectiveness of our method.
Zhijie Lin 0001, Kaiyang Lin, Shiling Chen, Linlin Li 0001, Zhou Zhao 0001
 AAAI2