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Record W1984865209 · doi:10.1109/icassp.2002.5743707

The relation between speech segment selectivity and source localization accuracy

2002· article· en· W1984865209 on OpenAlex
Parham Aarabi, Albarz Mahdavi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE International Conference on Acoustics Speech and Signal Processing · 2002
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMean squared errorCorrelationStatisticsRoot mean squareSpeech recognitionRelation (database)Phase (matter)SIGNAL (programming language)Power (physics)MathematicsMaximum likelihoodComputer sciencePattern recognition (psychology)Artificial intelligenceEngineeringPhysicsData mining

Abstract

fetched live from OpenAlex

An experimental analysis of the relation between speech signal segment power and the source direction-of-arrival-estimation accuracy is conducted. A total of 10 different speakers, including both male and female speakers, totaling to approximately 2 hours of speech are used to analyze the performance of the Phase Transform, the Maximum Likelihood, and the Unfiltered Cross Correlation time-delay estimation techniques. For female speakers, it is determined that the Phase Transform technique has a lower percentage of anomalies and a lower direction-of-arrival root mean-square error (DOA RMSE). Conversely, for male speakers, it is determined that the Unfiltered Cross Correlation has a lower percentage of anomalies although the Phase Transform has a lower DOA RMSE. The spatial distribution of the errors as well as the speech segment power relation to the errors are also presented.

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.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.001
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 head0.047
GPT teacher head0.287
Teacher spread0.240 · 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