Cited by 166Speech Recognition and SynthesisPhonetics and Phonology ResearchSpeech and Audio Processing

Which words are hard to recognize? Prosodic, lexical, and disfluency factors that increase speech recognition error rates

Open access
Goldwater, Sharon, Jurafsky, Dan, Manning, Christopher D.·2010·Speech Communication

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.specom.2009.10.001

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Past work has found that ASRs perform somewhat worse on conversational speech from male speakers than female speakers, likely due to male speakers using more informal style with shorter, more reduced pronunciations and more disfluencies.

    “Past work has also found that ASRs perform somewhat worse on conversational speech from male speakers than female speakers, likely due to male speakers using more informal style with shorter, more reduced pronunciations and more disfluencies (28, 29).”

    From Racial disparities in automated speech recognition (2020) · cites this paper as [28]

  • Age and gender are known to impact the performance of ASR systems.

    “age and gender are also known to impact the performance of ASR systems (28, 29).”

    From Racial disparities in automated speech recognition (2020) · cites this paper as [28]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

4 of the 45 references couldn’t be matched to a known paper.

Research works printed but not linked: 2

  1. Doddington, G., Schalk, T., 1981. Speech recognition: Turning theory to practice. IEEE Spectrum 18, 26-32.
  2. Pennock-Speck, B., 2005. The changing voice of women. In: de Leonardo et al., J. C. G. (Ed.), Actas del XXVIII Congreso Internacional de AEDEAN. Servei de Publicacions de la Universitat de Valencia, pp. 407-415.

Cited by

Sorted by citations

Authors
Goldwater, S., Jurafsky, D., & Manning, C. D.
Affiliations
  • University of Edinburgh (GB)
  • Stanford University (US)
Corresponding author
Goldwater, Sharon
Venue
Speech Communication
Publisher
Elsevier BV
Published
2010-03
Volume
52
Issue
3
Pages
181-200
ISSN
0167-6393 (ISSN-L)
OpenAlex
W2131694695
Cited by
166
References
45
Citations by year
2026: 5 · 2025: 8 · 2024: 6 · 2023: 12 · 2022: 12 · 2021: 9 · 2020: 12 · 2019: 10 · 2018: 15 · 2017: 12 · 2016: 9 · 2015: 17 · 2014: 12 · 2013: 8 · 2012: 6
FWCI
6.43
Type
journal article
Language
en
Open access
Locations
  • Speech Communication (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion)
  • HAL (submittedVersion, other-oa)
  • Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
Funders
  • Multidisciplinary University Research Initiative
  • Office of Naval Research
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords
speech recognition errors, prosody, disfluency, discourse markers, speaker differences, conversational speech

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review