Elamur Research⌕
☰
home / authors / Bhuvana Ramabhadran

Bhuvana Ramabhadran

Bhuvana Ramabhadran has 4 papers in our library, in 2017–2018, most of them on Speech & spoken language, Dialogue systems, English; often with Kartik Audhkhasi, George Saon, Michael Picheny.

4 papers in our library · 2017–2018

OpenAlex · ORCID · h-index 47 · 333 works · 9,624 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 65 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #2802

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2019–2025 · 42 paperscurrent🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States1999–2018 · 113 papers🏢IBMArmonk · United States1999–2018 · 91 papers🏢IBMMarkham · Canada2009 · 2 papers🎓University of HoustonHouston · United States1999–2005 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksSignal Processing · 4 worksPsychology · 1 workExperimental and Cognitive Psychology · 1 work

🧭 Topics 3Research subjects and languages the work is about.Speech & spoken language · 4 worksDialogue systems · 3 worksEnglish · 3 works

 Works by year

2010s

20181 paperJoint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition20173 papersBhuvana Ramabhadran's papers of 2017

 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. Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition (2018 · 73 citations)
    Speech recognizers lose accuracy when test speakers have an accent that differs from the training data. Earlier multi-accent acoustic models relied on accent information only indirectly, for example through separate phone sets per accent. Yang et al. instead train an accent identification (AID) network together with…

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. English Conversational Telephone Speech Recognition by Humans and MachinesInterspeechGeorge Saon, Gakuto Kurata, Tom Sercu, et al.2017 · 382 citations
  2. Direct Acoustics-to-Word Models for English Conversational Speech RecognitionInterspeechKartik Audhkhasi, Bhuvana Ramabhadran, George Saon, et al.2017 · 121 citations
  3. Building competitive direct acoustics-to-word models for English conversational speech recognitionIEEE International Conference on Acoustics Speech and Signal ProcessingKartik Audhkhasi, Brian Kingsbury, Bhuvana Ramabhadran, et al.2017 · 111 citations
  4. Joint Modeling of Accents and Acoustics for Multi-Accent Speech RecognitionIEEE International Conference on Acoustics Speech and Signal ProcessingXuesong Yang, Kartik Audhkhasi, Andrew Rosenberg, et al.2018 · 73 citations
  5. Large-scale Language Model Rescoring on Long-form DataarXiv:2306.08133 · ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · original ↗ · no summary yetTongzhou Chen et al.2023 · 21 citations

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 65 times (of the 141,840 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/bhuvana-ramabhadran/