Ian Lane

2.9k total citations · 1 hit paper
81 papers, 1.2k citations indexed

About

Ian Lane is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Ian Lane has authored 81 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Artificial Intelligence, 23 papers in Signal Processing and 10 papers in Computer Vision and Pattern Recognition. Recurrent topics in Ian Lane's work include Speech Recognition and Synthesis (41 papers), Natural Language Processing Techniques (33 papers) and Speech and dialogue systems (32 papers). Ian Lane is often cited by papers focused on Speech Recognition and Synthesis (41 papers), Natural Language Processing Techniques (33 papers) and Speech and dialogue systems (32 papers). Ian Lane collaborates with scholars based in United States, Japan and South Korea. Ian Lane's co-authors include Bing Liu, William Chan, Tanja Schultz, Alex Waibel, Tatsuya Kawahara, Wilson Tam, Jungsuk Kim, Tomoko Matsui, Satoshi Nakamura and Tong Yu and has published in prestigious journals such as IEEE Transactions on Audio Speech and Language Processing, Computer Speech & Language and Physiological Entomology.

In The Last Decade

Ian Lane

76 papers receiving 1.1k citations

Hit Papers

Attention-Based Recurrent Neural Network Models for Joint... 2016 2026 2019 2022 2016 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
    2016 378 citations Bing Liu, Ian Lane profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ian Lane United States 17 1.0k 205 200 61 42 81 1.2k
Alexander Gruenstein United States 18 927 0.9× 408 2.0× 112 0.6× 47 0.8× 78 1.9× 37 1.1k
Veton Këpuska United States 9 317 0.3× 136 0.7× 126 0.6× 44 0.7× 55 1.3× 32 575
Adam Janin United States 14 770 0.7× 439 2.1× 226 1.1× 35 0.6× 59 1.4× 40 1.1k
Md. Saiful Islam Bangladesh 15 449 0.4× 77 0.4× 212 1.1× 99 1.6× 21 0.5× 70 722
Ming Sun China 15 505 0.5× 160 0.8× 364 1.8× 31 0.5× 20 0.5× 29 795
Amr El-Desoky Mousa Germany 13 412 0.4× 156 0.8× 125 0.6× 28 0.5× 11 0.3× 25 588
Matúš Pleva Slovakia 14 321 0.3× 189 0.9× 127 0.6× 65 1.1× 68 1.6× 92 632
Hiroshi Shimodaira Japan 15 461 0.4× 275 1.3× 308 1.5× 21 0.3× 40 1.0× 71 776
Ricardo de Córdoba Spain 15 493 0.5× 233 1.1× 108 0.5× 35 0.6× 21 0.5× 88 709
Mondher Bouazizi Japan 17 829 0.8× 92 0.4× 181 0.9× 285 4.7× 78 1.9× 73 1.2k

Countries citing papers authored by Ian Lane

Since Specialization
Citations

This map shows the geographic impact of Ian Lane's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ian Lane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ian Lane more than expected).

Fields of papers citing papers by Ian Lane

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ian Lane. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ian Lane. The network helps show where Ian Lane may publish in the future.

Co-authorship network of co-authors of Ian Lane

This figure shows the co-authorship network connecting the top 25 collaborators of Ian Lane. A scholar is included among the top collaborators of Ian Lane based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ian Lane. Ian Lane is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lane, Ian, et al.. (2022). Online Continual Learning of End-to-End Speech Recognition Models. Interspeech 2022. 2668–2672. 6 indexed citations
2.
3.
Sun, Zheng, Aveek Purohit, Daniel P. Siewiorek, et al.. (2018). CoughLoc: Location-Aware Indoor Acoustic Sensing for Non-Intrusive Cough Detection. Figshare. 10 indexed citations
4.
Liu, Bing & Ian Lane. (2017). Iterative policy learning in end-to-end trainable task-oriented neural dialog models. 482–489. 41 indexed citations
5.
Liu, Bing & Ian Lane. (2017). Dialog context language modeling with recurrent neural networks. 5715–5719. 12 indexed citations
6.
Liu, Bing & Ian Lane. (2016). Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling. 685–689. 378 indexed citations breakdown →
7.
Han, Kyu J., et al.. (2016). Semi-Supervised Speaker Adaptation for In-Vehicle Speech Recognition with Deep Neural Networks. 3843–3847. 1 indexed citations
8.
Yu, Bo, Ian Lane, & Fang Chen. (2016). 3D Face Detection via Reconstruction Over Hierarchical Features for Single Face Situations. International Journal of Pattern Recognition and Artificial Intelligence. 30(4). 1655013–1655013.
9.
Gandhe, Ankur, Long Qin, Florian Metze, et al.. (2013). Using web text to improve keyword spotting in speech. Figshare. 1. 428–433. 12 indexed citations
10.
Lane, Ian, et al.. (2012). Machine Translation with Binary Feedback: a Large-Margin Approach. Conference of the Association for Machine Translation in the Americas. 12 indexed citations
11.
Buthpitiya, Senaka, Ian Lane, & Jike Chong. (2012). A parallel implementation of Viterbi training for acoustic models using graphics processing units. 1–10. 3 indexed citations
12.
Kilgour, Kevin, et al.. (2011). Unsupervised vocabulary selection for simultaneous lecture translation.. IWSLT. 214–221. 1 indexed citations
13.
Lane, Ian, et al.. (2010). Real-time spoken language identification and recognition for speech-to-speech translation.. IWSLT. 307–312. 2 indexed citations
14.
Lane, Ian, et al.. (2010). Tools for Collecting Speech Corpora via Mechanical-Turk. KITopen. 184–187. 24 indexed citations
15.
Paulik, Matthias, et al.. (2008). Sentence segmentation and punctuation recovery for spoken language translation. Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. 5105–5108. 20 indexed citations
16.
Zhao, Bing, Nguyễn Bách, Ian Lane, & Stephan Vogel. (2007). A Log-Linear Block Transliteration Model based on Bi-Stream HMMs. North American Chapter of the Association for Computational Linguistics. 364–371. 16 indexed citations
17.
Lane, Ian, Tatsuya Kawahara, Tomoko Matsui, & Satoshi Nakamura. (2005). Dialogue Speech Recognition by Combining Hierarchical Topic Classification and Language Model Switching(Spoken Language Systems, Corpus-Based Speech Technologies). IEICE Transactions on Information and Systems. 88(3). 446–453. 1 indexed citations
18.
Lane, Ian. (2005). Dialogue Speech Recognition by Combining Hierarchical Topic Classification and Language Model Switching. IEICE Transactions on Information and Systems. E88-D(3). 446–454. 6 indexed citations
19.
Lane, Ian, Tatsuya Kawahara, Tomoko Matsui, & Satoshi Nakamura. (2003). Hierarchical topic classification for dialog speech recognition based on language model switching. 429–432. 1 indexed citations
20.
Hodgson, Chris & Ian Lane. (1981). Some effects of photoperiod on larviposition and fresh weight‐gain in Myzus persicae. Physiological Entomology. 6(1). 21–25. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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