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.
Recent advances in deep learning for speech research at Microsoft
Countries citing papers authored by J. D. Williams
Since
Specialization
Citations
This map shows the geographic impact of J. D. Williams'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 J. D. Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. D. Williams more than expected).
This network shows the impact of papers produced by J. D. Williams. 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 J. D. Williams. The network helps show where J. D. Williams may publish in the future.
Co-authorship network of co-authors of J. D. Williams
This figure shows the co-authorship network connecting the top 25 collaborators of J. D. Williams.
A scholar is included among the top collaborators of J. D. Williams 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 J. D. Williams. J. D. Williams is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Metallinou, Angeliki, Dan Bohus, & J. D. Williams. (2013). Discriminative state tracking for spoken dialog systems. Meeting of the Association for Computational Linguistics. 466–475.22 indexed citations
6.
Heeman, Peter A., et al.. (2013). Continuously Predicting and Processing Barge-in During a Live Spoken Dialogue Task. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 384–393.13 indexed citations
7.
Williams, J. D.. (2013). Multi-domain learning and generalization in dialog state tracking. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 433–441.29 indexed citations
8.
Heeman, Peter A., et al.. (2012). Integrating Incremental Speech Recognition and POMDP-Based Dialogue Systems. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 275–279.15 indexed citations
9.
Williams, J. D.. (2011). An Empirical Evaluation of a Statistical Dialog System in Public Use. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 130–141.10 indexed citations
10.
Heeman, Peter A., et al.. (2011). Stability and Accuracy in Incremental Speech Recognition. Annual Meeting of the Special Interest Group on Discourse and Dialogue. 110–119.33 indexed citations
11.
Williams, J. D. & Steve Young. (2003). Using Wizard-of-Oz simulations to bootstrap Reinforcement - Learning based dialog management systems. Cambridge University Engineering Department Publications Database. 135–139.17 indexed citations
12.
Williams, J. D. & Joseph A. Pollizzi. (1992). StarView: The object oriented design of the ST DADS user interface. 52. 100.1 indexed citations
13.
Neu, Harold C. & J. D. Williams. (1988). New trends in urinary tract infections : the single-dose therapy. KARGER eBooks.8 indexed citations
Williams, J. D. & A. M. Geddes. (1976). Laboratory aspects of infections : [proceedings of the ninth International Congress of Chemotherapy held in London, July 1975]. Plenum Press eBooks.1 indexed citations
18.
Oxford, J. S. & J. D. Williams. (1975). Chemotherapy and control of influenza. Journal of Antimicrobial Chemotherapy. 1(4).3 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.