Evidence map›Paper›PMID 37701064›Full record

ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2023

ACOUSTICALLY-DRIVEN PHONEME REMOVAL THAT PRESERVES VOCAL AFFECT CUES.

Camille Noufi, Jonathan Berger, Michael Frank, Karen Parker, Daniel L Bowling

Abstract read
In one paragraph

Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. The Test of Auditory-Vocal Affect (TAVA) dataset.IEEE Workshop on Applications of Signal Processing to Audio and Acoustics : [proceedings]. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics · 2025
    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

5 authors.

Camille NoufiStanford University, Center for Computer Research in Music and Acoustics, Stanford, CA, USA.
Jonathan BergerStanford University, Center for Computer Research in Music and Acoustics, Stanford, CA, USA.
Michael FrankStanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, USA.
Karen ParkerStanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, USA.
Daniel L BowlingStanford School of Medicine, Department of Psychiatry and Behavioral Sciences, Stanford, CA, USA.

Funding

Advanced Assessment of Auditory-Vocal Affect in Autism with Speech and MusicK01MH122730 · NIMH · STANFORD UNIVERSITY · PI BOWLING, DANIEL LIU · 2020 to 2023
$718k
NIMH NIH HHS K01 MH122730
6 · The paper itself

Abstract

In this paper, we propose a method for removing linguistic information from speech for the purpose of isolating paralinguistic indicators of affect. The immediate utility of this method lies in clinical tests of sensitivity to vocal affect that are not confounded by language, which is impaired in a variety of clinical populations. The method is based on simultaneous recordings of speech audio and electroglotto-graphic (EGG) signals. The speech audio signal is used to estimate the average vocal tract filter response and amplitude envelop. The EGG signal supplies a direct correlate of voice source activity that is mostly independent of phonetic articulation. These signals are used to create a third signal designed to capture as much paralinguistic information from the vocal production system as possible-maximizing the retention of bioacoustic cues to affect-while eliminating phonetic cues to verbal meaning. To evaluate the success of this method, we studied the perception of corresponding speech audio and transformed EGG signals in an affect rating experiment with online listeners. The results show a high degree of similarity in the perceived affect of matched signals, indicating that our method is effective.

Indexed as

affectelectroglottagraphyparalanguagephoneme removalspeechvoice transformation

Identifiers

PMID37701064
PMCPMC10495117

What OpenQuestion holds

Textmetadata
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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.