James Glass

18.9k total citations · 2 hit papers
335 papers, 10.0k citations indexed

About

James Glass is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, James Glass has authored 335 papers receiving a total of 10.0k indexed citations (citations by other indexed papers that have themselves been cited), including 293 papers in Artificial Intelligence, 147 papers in Signal Processing and 35 papers in Computer Vision and Pattern Recognition. Recurrent topics in James Glass's work include Speech Recognition and Synthesis (199 papers), Natural Language Processing Techniques (121 papers) and Music and Audio Processing (115 papers). James Glass is often cited by papers focused on Speech Recognition and Synthesis (199 papers), Natural Language Processing Techniques (121 papers) and Music and Audio Processing (115 papers). James Glass collaborates with scholars based in United States, United Kingdom and Canada. James Glass's co-authors include Yu-An Chung, Victor W. Zue, Stephanie Seneff, Yaodong Zhang, Timothy J. Hazen, Yuan Gong, Yonatan Belinkov, Chia-ying Lee, Issam Bazzi and Wei-Ning Hsu and has published in prestigious journals such as Journal of Neuroscience, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

James Glass

327 papers receiving 8.7k citations

Hit Papers

AST: Audio Spectrogram Transformer 2021 2026 2022 2024 2021 2022 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Glass United States 48 7.9k 5.0k 1.5k 773 313 335 10.0k
Steve Renals United Kingdom 43 6.3k 0.8× 3.8k 0.8× 1.1k 0.8× 811 1.0× 305 1.0× 281 8.0k
Junichi Yamagishi Japan 48 8.3k 1.0× 7.1k 1.4× 2.0k 1.4× 956 1.2× 249 0.8× 388 10.8k
Andreas Stolcke United States 50 10.7k 1.4× 3.6k 0.7× 1.1k 0.7× 1.2k 1.5× 405 1.3× 230 12.0k
Alan W. Black United States 46 9.3k 1.2× 4.5k 0.9× 949 0.6× 1.2k 1.6× 388 1.2× 320 10.5k
Daniel Povey United States 50 13.8k 1.7× 10.7k 2.1× 1.3k 0.9× 951 1.2× 177 0.6× 178 15.7k
Alex Waibel United States 48 6.8k 0.9× 2.9k 0.6× 2.1k 1.5× 817 1.1× 246 0.8× 444 9.5k
Hervé Bourlard Switzerland 31 4.0k 0.5× 3.5k 0.7× 971 0.7× 564 0.7× 76 0.2× 299 5.6k
Tomoki Toda Japan 43 6.4k 0.8× 5.5k 1.1× 810 0.6× 757 1.0× 164 0.5× 425 7.3k
Alex Acero United States 43 7.9k 1.0× 5.6k 1.1× 2.0k 1.3× 433 0.6× 950 3.0× 233 10.7k
Satoshi Nakamura Japan 35 3.7k 0.5× 2.4k 0.5× 938 0.6× 520 0.7× 224 0.7× 677 6.5k

Countries citing papers authored by James Glass

Since Specialization
Citations

This map shows the geographic impact of James Glass'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 James Glass with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Glass more than expected).

Fields of papers citing papers by James Glass

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James Glass. 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 James Glass. The network helps show where James Glass may publish in the future.

Co-authorship network of co-authors of James Glass

This figure shows the co-authorship network connecting the top 25 collaborators of James Glass. A scholar is included among the top collaborators of James Glass 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 James Glass. James Glass 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.
He, Tianxing, Tianle Wang, Sachin Kumar, et al.. (2023). On the Blind Spots of Model-Based Evaluation Metrics for Text Generation. 12067–12097. 7 indexed citations
2.
Dawalatabad, Nauman, Yuan Gong, Sameer Khurana, Rhoda Au, & James Glass. (2022). Detecting Dementia from Long Neuropsychological Interviews. 5270–5283. 3 indexed citations
3.
Yeh, Sung-Lin, et al.. (2022). Autoregressive Predictive Coding: A Comprehensive Study. IEEE Journal of Selected Topics in Signal Processing. 16(6). 1380–1390. 12 indexed citations
4.
Laurent, Antoine, et al.. (2022). SAMU-XLSR: Semantically-Aligned Multimodal Utterance-Level Cross-Lingual Speech Representation. IEEE Journal of Selected Topics in Signal Processing. 16(6). 1493–1504. 18 indexed citations
5.
Gong, Yuan, Yu Jin, & James Glass. (2022). Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 151–155. 18 indexed citations
6.
Liu, Alexander, et al.. (2022). Cross-Modal Discrete Representation Learning. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 3013–3035. 24 indexed citations
7.
Gong, Yuan, Alexander H. Liu, Andrew Rouditchenko, & James Glass. (2022). UAVM: Towards Unifying Audio and Visual Models. IEEE Signal Processing Letters. 29. 2437–2441. 16 indexed citations
8.
Gong, Yuan, Yu-An Chung, & James Glass. (2021). AST: Audio Spectrogram Transformer. 571–575. 517 indexed citations breakdown →
9.
10.
Nadeem, Moin, Tianxing He, Kyunghyun Cho, & James Glass. (2020). A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation. 334–346. 6 indexed citations
11.
Lin, Honghuang, Cody Karjadi, Ting Fang Alvin Ang, et al.. (2020). Identification of digital voice biomarkers for cognitive health. SHILAP Revista de lepidopterología. 1(6). 406–417. 28 indexed citations
12.
Chung, Yu-An, et al.. (2019). Towards Unsupervised Speech-to-text Translation. 7170–7174. 22 indexed citations
13.
Chung, Yu-An & James Glass. (2018). Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech. 811–815. 72 indexed citations
14.
Chung, Yu-An, et al.. (2018). Unsupervised cross-modal alignment of speech and text embedding spaces. Neural Information Processing Systems. 31. 7365–7375. 13 indexed citations
15.
Harwath, David, Antonio Torralba, & James Glass. (2016). Unsupervised learning of spoken language with visual context. DSpace@MIT (Massachusetts Institute of Technology). 29. 1858–1866. 112 indexed citations
16.
Lee, Chia-ying & James Glass. (2012). A Nonparametric Bayesian Approach to Acoustic Model Discovery. Meeting of the Association for Computational Linguistics. 40–49. 126 indexed citations
17.
Shum, Stephen, Najim Dehak, Réda Dehak, & James Glass. (2010). Unsupervised Speaker Adaptation based on the Cosine Similarity for Text-Independent Speaker Verification.. 16. 38 indexed citations
18.
Baker, James, et al.. (2009). Updated MINDS Report on Speech Recognition and Understanding. IEEE Signal Processing Magazine. 26. 16 indexed citations
19.
Glass, James, Timothy J. Hazen, Scott Cyphers, et al.. (2007). Recent progress in the MIT spoken lecture processing project. 2553–2556. 114 indexed citations
20.
Leung, Hong, James Glass, Michael Phillips, & Victor W. Zue. (1990). Phonetic Classification and Recognition Using the Multi-Layer Perceptron. Neural Information Processing Systems. 3. 248–254. 1 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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