Larry Heck
- Artificial Intelligence top 0.2%
- Natural Language Processing Techniques 32
- Topic Modeling 31
- Speech and dialogue systems 28
- Speech Recognition and Synthesis 27
- Domain Adaptation and Few-Shot Learning 7
- Semantic Web and Ontologies 7
- Signal Processing top 1%
- Speech and Audio Processing 22
- Music and Audio Processing 16
- Information Systems top 1%
- Computer Science Applications top 10%
Larry Heck
89 papers receiving 3.0k citations
Hit Papers
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 2.7k
- Signal Processing 693
- Computer Vision and Pattern Recognition 715
- Information Systems 633
- Computer Science Applications 46
Countries citing papers authored by Larry Heck
This map shows the geographic impact of Larry Heck'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 Larry Heck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Larry Heck more than expected).
Fields of papers citing papers by Larry Heck
This network shows the impact of papers produced by Larry Heck. 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 Larry Heck. The network helps show where Larry Heck may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Larry Heck, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 13 | |
| 4 | 2023 | 2 | |
| 5 | 2022 | 14 | |
| 6 | 2021 | 12 | |
| 7 | Efficient Incremental Learning for Mobile Object Detection | 2019 | 4 |
| 8 | 2019 | 2 | |
| 9 | A Unit Selection Methodology for Music Generation Using Deep Neural Networks. | 2016 | 2 |
| 10 | 2014 | 22 | |
| 11 | 2014 | 2 | |
| 12 | Zero-Shot Learning and Clustering for Semantic Utterance Classification | 2013 | 9 |
| 13 | Research Challenges and Opportunities in Mobile Applications | 2011 | 5 |
| 14 | 2011 | 22 | |
| 15 | 2011 | 5 | |
| 16 | Study on the effect of lexical mismatch in text-dependent speaker verification. | 2004 | 3 |
| 17 | 2003 | 12 | |
| 18 | Integrating speaker and speech recognizers: Automatic identity claim capture for speaker verification. | 2001 | 3 |
| 19 | A Model-Based Transformational Approach to Robust Speaker Recognition | 2000 | 4 |
| 20 | Large-scale, broadband actuator selection for active noise control | 1994 | 4 |
About Larry Heck
Larry Heck is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics and Music, having authored 94 papers that have together received 3.3k indexed citations. Recurring topics across this work include Natural Language Processing Techniques (32 papers), Topic Modeling (31 papers), Speech and dialogue systems (28 papers), Speech Recognition and Synthesis (27 papers), Speech and Audio Processing (22 papers), Music and Audio Processing (16 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Semantic Web and Ontologies (7 papers). The work is most often cited by research in Artificial Intelligence (2.7k citations), Signal Processing (693 citations), Computer Vision and Pattern Recognition (715 citations), Information Systems (633 citations) and Computer Science Applications (46 citations). Larry Heck has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Xiaodong He, Li Deng, Dilek Hakkani‐Tür, Po-Sen Huang, Alex Acero, Jianfeng Gao, Gökhan Tür, M. Weintraub, Kemal Sönmez and Elizabeth Shriberg. Their work appears in journals such as IEEE Signal Processing Magazine, Journal of vibration and acoustics, Speech Communication, Digital Signal Processing and Proceedings of the ACM on Human-Computer Interaction.
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.