Michael Picheny

6.5k total citations · 3 hit papers
142 papers, 4.0k citations indexed

About

Michael Picheny is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Michael Picheny has authored 142 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Artificial Intelligence, 88 papers in Signal Processing and 17 papers in Computer Vision and Pattern Recognition. Recurrent topics in Michael Picheny's work include Speech Recognition and Synthesis (103 papers), Speech and Audio Processing (69 papers) and Music and Audio Processing (58 papers). Michael Picheny is often cited by papers focused on Speech Recognition and Synthesis (103 papers), Speech and Audio Processing (69 papers) and Music and Audio Processing (58 papers). Michael Picheny collaborates with scholars based in United States, Germany and Czechia. Michael Picheny's co-authors include N. I. Durlach, Louis D. Braida, D. Nahamoo, George Saon, Bhuvana Ramabhadran, Hagen Soltau, Arthur Nádas, L.R. Bahl, Yuqing Gao and Tara N. Sainath and has published in prestigious journals such as The Journal of the Acoustical Society of America, Computer and IEEE Signal Processing Magazine.

In The Last Decade

Michael Picheny

137 papers receiving 3.4k citations

Hit Papers

align trajectories sort by overperformance

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.

Speaking Clearly for the Hard of Hearing II

1986 516 citations Michael Picheny et al. profile →
Speaker adaptation of neural network acoustic models using i-vectors

2013 406 citations George Saon, D. Nahamoo et al. profile →
Deep Belief Networks using discriminative features for phone recognition

2011 196 citations Tara N. Sainath, Bhuvana Ramabhadran et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Michael Picheny	2.9k	2.2k	855	590	365	142	4.0k
Mark Hasegawa‐Johnson	2.5k 0.9×	2.5k 1.1×	1.3k 1.6×	529 0.9×	901 2.5×	296	4.8k
Carol Espy-Wilson	2.0k 0.7×	1.9k 0.9×	1.2k 1.4×	212 0.4×	233 0.6×	154	2.9k
Ronald A. Cole	2.0k 0.7×	1.3k 0.6×	1.1k 1.2×	638 1.1×	385 1.1×	142	3.4k
Douglas O’Shaughnessy	2.2k 0.8×	2.2k 1.0×	703 0.8×	255 0.4×	541 1.5×	258	3.4k
Alexandros Potamianos	2.4k 0.8×	1.6k 0.7×	1.0k 1.2×	318 0.5×	727 2.0×	169	4.1k
Tan Lee	1.5k 0.5×	1.2k 0.5×	415 0.5×	261 0.4×	203 0.6×	255	2.2k
Sungbok Lee	3.4k 1.2×	2.7k 1.2×	4.3k 5.1×	822 1.4×	913 2.5×	122	6.5k
Victor W. Zue	2.8k 1.0×	1.4k 0.6×	665 0.8×	176 0.3×	296 0.8×	169	3.5k
Shrikanth Narayanan	979 0.3×	1.0k 0.5×	611 0.7×	167 0.3×	319 0.9×	70	2.2k
Jianwu Dang	1.7k 0.6×	1.3k 0.6×	982 1.1×	342 0.6×	323 0.9×	347	3.0k

Countries citing papers authored by Michael Picheny

Since Specialization

Citations

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

Fields of papers citing papers by Michael Picheny

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Picheny

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

All Works

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20 of 20 papers shown

Meng, Zhong, Rohit Prabhavalkar, Andrew Rosenberg, et al.. (2023). Improving Joint Speech-Text Representations Without Alignment. 1354–1358. 1 indexed citations

Picheny, Michael, et al.. (2023). A Comparison of Semi-Supervised Learning Techniques for Streaming ASR at Scale. 1–5.

Chen, Brian, Andrew Rouditchenko, Hilde Kuehne, et al.. (2021). Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7992–8001. 46 indexed citations

Cui, Xiaodong, Wei Zhang, Ulrich Finkler, et al.. (2020). Distributed Training of Deep Neural Network Acoustic Models for Automatic Speech Recognition: A comparison of current training strategies. IEEE Signal Processing Magazine. 37(3). 39–49. 17 indexed citations

Audhkhasi, Kartik, Dhiraj Joshi, David Harwath, et al.. (2019). Grounding Spoken Words in Unlabeled Video. Computer Vision and Pattern Recognition. 29–32. 9 indexed citations

Picheny, Michael, et al.. (2019). Identifying Mood Episodes Using Dialogue Features from Clinical Interviews. 1926–1930. 11 indexed citations

Chaudhari, Upendra V. & Michael Picheny. (2009). Improved vocabulary independent search with approximate match based on Conditional Random Fields. 416–420. 3 indexed citations

Eide, Ellen, Raul Castro Fernandez, Ron Hoory, et al.. (2006). The IBM Submission to the 2006 Blizzard Text-to-Speech Challenge. 49–52. 3 indexed citations

Eide, Ellen, et al.. (2004). A corpus-based approach to expressive speech synthesis.. SSW. 79–84. 49 indexed citations

10.

Zhou, Bowen, et al.. (2004). A hand-held speech-to-speech translation system. 664–669. 13 indexed citations

11.

Zhou, Bowen, et al.. (2002). Statistical Natural Language Generation for Speech-to-Speech Machine Translation. Conference of the International Speech Communication Association. 13 indexed citations

12.

Oard, Douglas W., Dina Demner‐Fushman, Bill Byrne, et al.. (2002). Cross-Language Access to Recorded Speech in the MALACH Project. 1 indexed citations

13.

Soergel, Dagobert, Douglas W. Oard, Bill Byrne, et al.. (2002). Supporting access to large digital oral history archives. 2 indexed citations

14.

Zhou, Bowen, et al.. (2002). Statistical natural language generation for speech-to-speech machine translation systems. 1897–1900. 5 indexed citations

15.

Bahl, L.R., Martin Franz, P.S. Gopalakrishnan, et al.. (2002). Performance of the IBM large vocabulary continuous speech recognition system on the ARPA Wall Street Journal task. 1. 41–44. 44 indexed citations

16.

Ittycheriah, Abraham, Martin Franz, Bhuvana Ramabhadran, et al.. (2001). Current status of the IBM Trainable Speech Synthesis System.. SSW. 207. 20 indexed citations

17.

Novák, Miroslav & Michael Picheny. (1999). Speed improvement of the time-asynchronous acoustic fast match. 1115–1118. 2 indexed citations

18.

Gao, Yuqing, M. Padmanabhan, & Michael Picheny. (1997). Speaker adaptation based on pre-clustering training speakers. 2091–2094. 24 indexed citations

19.

Bellegarda, J.R., P.V. de Souza, Arthur Nádas, et al.. (1992). Robust speaker adaptation using a piecewise linear acoustic mapping. 445–448 vol.1. 14 indexed citations

20.

Bahl, L.R., P.V. de Souza, P.S. Gopalakrishnan, D. Nahamoo, & Michael Picheny. (1992). A fast match for continuous speech recognition using allophonic models. 17–20 vol.1. 16 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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