🧑‍🔬 Michael Picheny

🏛️ Affiliations

🎓New York UniversityNew York · United States2021–2026 · 11 paperscurrent🎓Courant Institute of Mathematical SciencesNew York · United States2026 · 1 papercurrent🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States1986–2020 · 101 papers🏢IBMArmonk · United States1989–2021 · 65 papers🎓Massachusetts Institute of TechnologyCambridge · United States1976–1986 · 7 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. English Conversational Telephone Speech Recognition by Humans and Machines (2017 · 382 citations)
   The paper tests the claim that machines reached human-level accuracy on conversational telephone speech. It also reports a stronger IBM recognizer. The authors hired the transcription vendor Appen to run their own human benchmark on the Switchboard (SWB) and CallHome (CH) subsets of the NIST Hub5 2000 test set. Three…
2. Direct Acoustics-to-Word Models for English Conversational Speech Recognition (2017 · 121 citations)
   The paper asks whether one neural network can turn speech directly into words, with no phone inventory, pronunciation dictionary, language model or decoder.
3. Building competitive direct acoustics-to-word models for English conversational speech recognition (2017 · 111 citations)
   Direct acoustics-to-word (A2W) models map speech straight to whole words with one neural network. They need no pronunciation lexicon, no decoder and no separately trained language model. Earlier work suggested such models need vastly more audio than phone- or character-based systems, and the authors' own previous A2W…
4. The IBM 2015 English Conversational Telephone Speech Recognition System (2015 · 96 citations)
   The paper reports how IBM improved its English recognizer for conversational telephone speech. The benchmark is the Switchboard part of the Hub5-2000 evaluation. Training used 1975 hours of two-party phone conversations from Switchboard 1, Fisher and CallHome. The authors argue that no single trick is enough and that…
5. English Broadcast News Speech Recognition by Humans and Machines (2019 · 12 citations)
   The paper asks whether deep-learning recipes that brought conversational telephone speech (CTS) recognition near human level also work for English broadcast news (BN).

🤝 Co-authors here

10 co-authors with a page, the most papers together first.

• George Saon ×5
• Bhuvana Ramabhadran ×3
• Kartik Audhkhasi ×3
• Brian Kingsbury ×2
• Gakuto Kurata ×2
• Samuel Thomas ×2
• Hong-Kwang J. Kuo
• Steven Rennie
• Tom Sercu
• Zoltan Tuske

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Interspeech ×3: Interspeech 2017 ×2, Interspeech 2015
• IEEE International Conference on Acoustics Speech and Signal Processing ×2: IEEE International Conference on Acoustics Speech and Signal Processing 2019, IEEE International Conference on Acoustics Speech and Signal Processing 2017

🔗 Cited by

Papers of our library cite their works 59 times (of the 141,840 papers whose bibliographies we read); 12 citing papers with a page here →.

Updated 24 October 2026

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