Evidence map›Paper›PMID 35326294›Full record

ArticleBrain sciences2022

Computational Modelling of Tone Perception Based on Direct Processing of

Yue Chen, Yingming Gao, Yi Xu

Abstract read
In one paragraph

Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Yue ChenDepartment of Speech, Hearing and Phonetic Sciences, University College London, London WC1N 1PF, UK.ORCID 0000-0001-7528-7593
Yingming GaoInstitute of Acoustics and Speech Communication, TU Dresden, 01069 Dresden, Germany.ORCID 0000-0001-5881-3723
Yi XuDepartment of Speech, Hearing and Phonetic Sciences, University College London, London WC1N 1PF, UK.ORCID 0000-0002-8541-2658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It has been widely assumed that in speech perception it is imperative to first detect a set of distinctive properties or features and then use them to recognize phonetic units like consonants, vowels, and tones. Those features can be auditory cues or articulatory gestures, or a combination of both. There have been no clear demonstrations of how exactly such a two-phase process would work in the perception of continuous speech, however. Here we used computational modelling to explore whether it is possible to recognize phonetic categories from syllable-sized continuous acoustic signals of connected speech without intermediate featural representations. We used Support Vector Machine (SVM) and Self-organizing Map (SOM) to simulate tone perception in Mandarin, by either directly processing

Indexed as

Mandarin tonesspeech perceptiontone featurestone recognition

Identifiers

PMID35326294
PMCPMC8946547

What OpenQuestion holds

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Registered trials

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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.