M.M. Hochberg

626 total citations
19 papers, 339 citations indexed

About

M.M. Hochberg is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, M.M. Hochberg has authored 19 papers receiving a total of 339 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Artificial Intelligence, 16 papers in Signal Processing and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in M.M. Hochberg's work include Speech Recognition and Synthesis (18 papers), Speech and Audio Processing (14 papers) and Music and Audio Processing (8 papers). M.M. Hochberg is often cited by papers focused on Speech Recognition and Synthesis (18 papers), Speech and Audio Processing (14 papers) and Music and Audio Processing (8 papers). M.M. Hochberg collaborates with scholars based in United Kingdom, United States and France. M.M. Hochberg's co-authors include Steve Renals, Tony Robinson, Ciro Martins, Luís Nunes, João P. Neto, Luı́s B. Almeida, H.F. Silverman, Anthony J. Robinson, Yoshihiko Gotoh and T. Robinson and has published in prestigious journals such as IEEE Transactions on Speech and Audio Processing, Neural Information Processing Systems and ERA.

In The Last Decade

M.M. Hochberg

17 papers receiving 293 citations

Peers

Countries citing papers authored by M.M. Hochberg

Since Specialization

Citations

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

Fields of papers citing papers by M.M. Hochberg

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M.M. Hochberg

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

All Works

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19 of 19 papers shown

#	Work	Indexed citations
1	Speaker-trained recognition using allophonic enrollment models 2005 ·(unknown), M.M. Hochberg, C.J. Leggetter	0
2	Real-time recognition of broadcast radio speech 2002 ·(unknown), (unknown), (unknown), M.M. Hochberg, Beth Logan, A.J. Robinson, (unknown)	4
3	Using MAP estimated parameters to improve HMM speech recognition performance 2002 ·Yoshihiko Gotoh, M.M. Hochberg, H.F. Silverman	2
4	IPA: improved phone modelling with recurrent neural networks 2002 ·ERA ·T. Robinson, M.M. Hochberg, Steve Renals	22
5	Efficient evaluation of the LVCSR search space using the NOWAY decoder 2002 ·ERA ·Steve Renals, M.M. Hochberg	12
6	Efficient search using posterior phone probability estimates 2002 ·Steve Renals, M.M. Hochberg	20
7	Recent improvements to the ABBOT large vocabulary CSR system 2002 ·M.M. Hochberg, Steve Renals, A.J. Robinson, (unknown)	14
8	Incremental MAP estimation of HMMs for efficient training and improved performance 2002 ·(unknown), M.M. Hochberg, (unknown), H.F. Silverman	6
9	Connectionist model combination for large vocabulary speech recognition 2002 ·ERA ·M.M. Hochberg, (unknown), Steve Renals, A.J. Robinson	4
10	Start-synchronous search for large vocabulary continuous speech recognition 1999 ·IEEE Transactions on Speech and Audio Processing ·Steve Renals, M.M. Hochberg	22
11	Efficient training algorithms for HMMs using incremental estimation 1998 ·IEEE Transactions on Speech and Audio Processing ·Yoshihiko Gotoh, M.M. Hochberg, H.F. Silverman	25
12	Context-Dependent Classes in a Hybrid Recurrent Network-HMM Speech Recognition System 1995 ·Neural Information Processing Systems ·(unknown), Anthony J. Robinson, M.M. Hochberg	34
13	Speaker-adaptation for hybrid HMM-ANN continuous speech recognition system 1995 ·João P. Neto, Luı́s B. Almeida, M.M. Hochberg, Ciro Martins, Luís Nunes, Steve Renals, Tony Robinson	130
14	The 1994 Abbot hybrid connectionist-HMM large vocabulary recognition system. 1995 ·M.M. Hochberg, (unknown), Steve Renals, Tony Robinson, (unknown)	18
15	Using gamma filters to model temporal dependencies in speech 1994 ·Steve Renals, M.M. Hochberg	1
16	Large vocabulary continuous speech recognition using a hybrid connectionist-HMM system 1994 ·M.M. Hochberg, Steve Renals, (unknown), (unknown)	13
17	Learning Temporal Dependencies in Connectionist Speech Recognition 1993 ·ERA ·Steve Renals, M.M. Hochberg, Tony Robinson	1
18	Distributed hidden Markov model training on loosely-coupled multiprocessor networks 1992 ·Jonathan Foote, M.M. Hochberg, Peter M. Athanas, (unknown), M. Wazlowski, H.F. Silverman	2
19	Hidden Markov model/neural network training techniques for connected alphadigit speech recognition 1991 ·M.M. Hochberg, (unknown), Jonathan Foote, H.F. Silverman	9

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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