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Record W2103408436 · doi:10.1109/imtc.2005.1604216

Performance of Phase-Space Voicing-State Classification for Co-Channel Speech

2006· article· en· W2103408436 on OpenAlex

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

Venue2005 IEEE Instrumentationand Measurement Technology Conference Proceedings · 2006
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsCarleton University
Fundersnot available
KeywordsVoiceSpeech recognitionComputer scienceA priori and a posterioriChannel (broadcasting)Speech enhancementNoise (video)Speech processingBackground noiseArtificial intelligenceTelecommunications

Abstract

fetched live from OpenAlex

This paper discusses the performance of a classification algorithm that is capable of determining the voicing-state of co-channel speech. The algorithm uses some features of the reconstructed phase-space of the speech data as a measure to identify the three voicing-states of co-channel speech; unvoiced/unvoiced (U/U), voiced/unvoiced (V/U), and voiced/voiced (V/V). The proposed method requires neither a priori information nor speech training data. Nonetheless, simulation results show enhanced performance in identifying the three voicing-states compared to other existing techniques. The algorithm also shows a reliable performance for different SIR values as well as different levels of background noise

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.048
GPT teacher head0.280
Teacher spread0.233 · 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