Evidence map›Paper›PMID 29857765›Full record

ArticleThe Journal of the Acoustical Society of America2018

Glimpsing speech interrupted by speech-modulated noise.

Rachel E Miller, Bobby E Gibbs, Daniel Fogerty

Abstract read
In one paragraph

Article in The Journal of the Acoustical Society of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Speech Intelligibility Prediction using Spectro-Temporal Modulation Analysis.IEEE/ACM transactions on audio, speech, and language processing · 2021
    Article
  5. Review
  6. The Perceptual Contribution of Consonants and Vowels to Sentence Recognition: Effect of Dialect Variation in American English.Proceedings of the ... International Congress of Phonetic Sciences. International Congress of Phonetic Sciences · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rachel E MillerDepartment of Communication Sciences and Disorders, University of South Carolina, 1229 Marion Street, Columbia, South Carolina 29201, USA.
Bobby E GibbsDepartment of Communication Sciences and Disorders, University of South Carolina, 1229 Marion Street, Columbia, South Carolina 29201, USA.
Daniel FogertyDepartment of Communication Sciences and Disorders, University of South Carolina, 1229 Marion Street, Columbia, South Carolina 29201, USA.

Funding

Maximizing speech recognition under adverse listening conditionsR01DC015465 · NIDCD · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI FOGERTY, DANIEL · 2016 to 2020
$1.6M
Maximizing Speech Recognition Under Adverse Listening ConditionsR03DC012506 · NIDCD · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI FOGERTY, DANIEL · 2012 to 2014
$313k
NIDCD NIH HHS R01 DC015465NIDCD NIH HHS R03 DC012506
6 · The paper itself

Abstract

Everyday environments frequently present speech in modulated noise backgrounds, such as from a competing talker. Under such conditions, temporal glimpses of speech may be preserved at favorable signal-to-noise ratios during the amplitude dips of the masker. Speech recognition is determined, in part, by these speech glimpses. However, properties of the noise when it dominates the speech may also be important. This study interrupted speech to provide either high-intensity or low-intensity speech glimpses derived from measurements of speech-on-speech masking. These interrupted intervals were deleted and subsequently filled by steady-state noise or one of four different types of noise amplitude modulated by the same or different sentence. Noise was presented at two different levels. Interruption by silence was also examined. Speech recognition was best with high-intensity glimpses and improved when the noise was modulated by missing high-intensity segments. Additional noise conditions detailed significant interactions between the noise level and glimpsed speech level. Overall, high-intensity speech segments, and the amplitude modulation (AM) of the segments, are crucial for speech recognition. Speech recognition is further influenced by the properties of the competing noise (i.e., level and AM) which interact with the glimpsed speech level. Acoustic properties of both speech-dominated and noise-dominated intervals of speech-noise mixtures determine speech recognition.

Indexed as

NoiseAcoustic StimulationAdultFemaleHumansMalePerceptual MaskingRandom AllocationSpeech PerceptionYoung Adult

Identifiers

PMID29857765
PMCPMC5966307

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.