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Record W2107687614 · doi:10.1109/icassp.2013.6639210

Phonetic subspace adaptation for automatic speech recognition

2013· article· en· W2107687614 on OpenAlexaff
Sina Hamidi Ghalehjegh, Richard C. Rose

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsMcGill University
Fundersnot available
KeywordsSubspace topologyComputer scienceProjection (relational algebra)Adaptation (eye)Speech recognitionArtificial intelligenceRandom subspace methodPattern recognition (psychology)VocabularyLinear subspaceAlgorithmMathematics

Abstract

fetched live from OpenAlex

An approach is proposed for adapting subspace projection vectors in the subspace Gaussian mixturemodel (SGMM) [1]. Subword models in the SGMM are composed of states, each of which are parametrized using a small number of subspace projection vectors. It is shown here that these projection vectors provide a compact and well-behaved characterization of phonetic information in speech. A regression based subspace vector adaptation approach is proposed for adapting these parameters. The performance of this approach is evaluated for unsupervised speaker adaptation on two large vocabulary speech corpora.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.993
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.045
GPT teacher head0.240
Teacher spread0.196 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreMethods

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

Citations7
Published2013
Admission routes1
Has abstractyes

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