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Record W2133225641 · doi:10.14288/1.0086923

The effects of noise on identification of topic changes in discourse

2009· article· en· W2133225641 on OpenAlexaff
Glynnis Tidball

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsActive listeningNoise (video)Identification (biology)Discourse analysisAcousticsBackground noisePsychologySpeech recognitionComputer scienceLinguisticsCommunicationArtificial intelligencePhysicsBiology

Abstract

fetched live from OpenAlex

The purpose of the present study was to investigate how adverse listening conditions affect the ability of normal-hearing listeners to identify the boundaries between discourse topics, or when "what is being talked about" has changed. Twelve subjects (21 to 35 years) listened to digitized recordings of a single speaker's monologues presented in three background noise conditions (+5, 0 and -5 dB S:N). Subjects were asked to push a button when they thought that a change of topic was about to occur in the monologue. Subject responses were analyzed for the latency of topic boundary identification and the number and location of responses. The role of prosodic cues in the identification of topic boundaries was also evaluated. It was found that as the listening condition became less favourable, listeners were slower to identify topic boundaries, were less certain as to where topic boundaries occurred, and relied more heavily on cues to topic initiation than on cues to topic termination for identification of topic boundaries. It was also shown that as the signal-tonoise ratio decreased, listeners were less able to utilize cues to topic boundary that are present in low amplitude utterances such as pitch range and contour, laryngealization and pre-boundary syllable lengthening, but that listeners relied on the prosodic cue of pause duration to identify topic boundaries equally in all three listening conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.183
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

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