H. Gish

4.8k total citations · 1 hit paper
86 papers, 3.2k citations indexed

About

H. Gish is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, H. Gish has authored 86 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 69 papers in Artificial Intelligence, 45 papers in Signal Processing and 14 papers in Computer Vision and Pattern Recognition. Recurrent topics in H. Gish's work include Speech Recognition and Synthesis (48 papers), Speech and Audio Processing (31 papers) and Natural Language Processing Techniques (23 papers). H. Gish is often cited by papers focused on Speech Recognition and Synthesis (48 papers), Speech and Audio Processing (31 papers) and Natural Language Processing Techniques (23 papers). H. Gish collaborates with scholars based in United States, Hong Kong and Sweden. H. Gish's co-authors include S. Roucos, J. Makhoul, John R. Pierce, Man-Hung Siu, Ellen Eide, Kenney Ng, Ján Rohlíček, D. Cochran, Alexander Mielke and W. Russell and has published in prestigious journals such as Proceedings of the IEEE, IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing.

In The Last Decade

H. Gish

82 papers receiving 2.7k citations

Hit Papers

align trajectories

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.

Vector quantization in speech coding

1985 598 citations S. Roucos, H. Gish et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
H. Gish United States	25	2.2k	1.9k	978	184	164	86	3.2k
Frank Seide China	24	3.5k 1.6×	2.1k 1.1×	765 0.8×	227 1.2×	275 1.7×	95	4.3k
S. Levinson United States	22	2.3k 1.0×	1.8k 0.9×	767 0.8×	103 0.6×	91 0.6×	86	3.2k
Joe Frankel United Kingdom	18	1.3k 0.6×	1.1k 0.6×	571 0.6×	191 1.0×	229 1.4×	38	2.5k
Özgür Çetin United States	12	919 0.4×	780 0.4×	549 0.6×	185 1.0×	228 1.4×	27	2.0k
Peder A. Olsen United States	20	1.1k 0.5×	848 0.4×	429 0.4×	82 0.4×	95 0.6×	82	1.9k
Nash Borges United States	6	669 0.3×	593 0.3×	527 0.5×	190 1.0×	221 1.3×	9	1.7k
Lin-shan Lee Taiwan	23	2.4k 1.1×	1.4k 0.7×	437 0.4×	61 0.3×	66 0.4×	269	2.8k
Xuemin Chi China	7	638 0.3×	575 0.3×	551 0.6×	189 1.0×	226 1.4×	14	1.8k
Pedro J. Moreno United States	28	2.1k 1.0×	1.4k 0.7×	955 1.0×	49 0.3×	50 0.3×	78	3.0k
M.H. Ackroyd United Kingdom	13	758 0.3×	1.2k 0.6×	593 0.6×	205 1.1×	323 2.0×	33	2.3k

Countries citing papers authored by H. Gish

Since Specialization

Citations

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

Fields of papers citing papers by H. Gish

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of H. Gish

This figure shows the co-authorship network connecting the top 25 collaborators of H. Gish. A scholar is included among the top collaborators of H. Gish 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 H. Gish. H. Gish is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Siu, Man-Hung, et al.. (2013). Unsupervised training of an HMM-based self-organizing unit recognizer with applications to topic classification and keyword discovery. Computer Speech & Language. 28(1). 210–223. 50 indexed citations

2.

Siu, Man-Hung, et al.. (2011). Unsupervised audio patterns discovery using HMM-based self-organized units. 2333–2336. 14 indexed citations

3.

Siu, Man-Hung, et al.. (2010). Improved topic classification and keyword discovery using an HMM-based speech recognizer trained without supervision. 2838–2841. 20 indexed citations

4.

Miller, David R., Chia-Lin Kao, Owen Kimball, et al.. (2007). Rapid and accurate spoken term detection. 314–317. 191 indexed citations

5.

Gish, H., M. Krasner, W. Russell, & Jared J. Wolf. (2005). Methods and experiments for text-independent speaker recognition over telephone channels. 11. 865–868. 4 indexed citations

6.

Gish, H., et al.. (2005). Investigation of text-independent speaker indentification over telephone channels. 10. 379–382. 9 indexed citations

7.

Rohlíček, Ján, W. Russell, Salim Roukos, & H. Gish. (2003). Continuous hidden Markov modeling for speaker-independent word spotting. International Conference on Acoustics, Speech, and Signal Processing. 627–630. 133 indexed citations

8.

Gish, H. & D. Cochran. (2003). Generalized coherence (signal detection). 2745–2748. 26 indexed citations

9.

Zavaliagkos, G., J. McDonough, David R. Miller, et al.. (2002). The BBN Byblos 1997 large vocabulary conversational speech recognition system. 2. 905–908. 12 indexed citations

10.

McDonough, J., Tasos Anastasakos, G. Zavaliagkos, & H. Gish. (2002). Speaker-adapted training on the Switchboard Corpus. 2. 1059–1062. 3 indexed citations

11.

Eide, Ellen & H. Gish. (2002). A parametric approach to vocal tract length normalization. 1. 346–348. 174 indexed citations

12.

Kimball, Owen, et al.. (2002). Extracting descriptive noun phrases from conversational speech. IEEE International Conference on Acoustics Speech and Signal Processing. I–33. 3 indexed citations

13.

Siu, Man-Hung & H. Gish. (1999). Evaluation of word confidence for speech recognition systems. Computer Speech & Language. 13(4). 299–319. 42 indexed citations

14.

Iyer, Rishabh, Mari Ostendorf, & H. Gish. (1997). Using out-of-domain data to improve in-domain language models. IEEE Signal Processing Letters. 4(8). 221–223. 50 indexed citations

15.

Siu, Man-Hung, H. Gish, & Fred Richardson. (1997). Improved estimation, evaluation and applications of confidence measures for speech recognition. 831–834. 47 indexed citations

16.

Gish, H., et al.. (1995). Two novel language model estimation techniques for statistical language identification. 1363–1366. 8 indexed citations

17.

Gish, H., et al.. (1994). Issues in topic identification on the switchboard corpus. 2163–2166. 8 indexed citations

18.

Ng, Kenney, et al.. (1993). Phonetic-based word spotter: various configurations and application to event spotting. 1057–1060. 16 indexed citations

19.

Gish, H., Ronald A. Mucci, & Yaakov Bar‐Shalom. (1985). A New Approach to Target Detection and Estimation in Clutter. American Control Conference. 22(22). 1039–1046. 4 indexed citations

20.

Gish, H.. (1967). OPTIMUM QUANTIZATION OF RANDOM SEQUENCES.. Defense Technical Information Center (DTIC). 6 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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