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Record W1899038514 · doi:10.1109/iembs.1990.691723

Speech Coding Based On Vector Quantization For A Tactile Vocoder Hearing Device

2005· article· en· W1899038514 on OpenAlexaff
S. Pourmehdi, J. Mouïne, Mohamad Sawan, J. Lajoie, F. Duval

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpeech and Audio Processing
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSpeech codingComputer scienceSpeech recognitionDigital signal processingCodec2CodecVoice activity detectionQuantization (signal processing)Speech processingCoding (social sciences)Vector quantizationSignal processingLinear predictive codingSpeech synthesisComputer hardwareArtificial intelligenceComputer vision

Abstract

fetched live from OpenAlex

A portable digital speech processing system for deaf patients is described in the paper. This real-time processing system provides appropriate information to a ten-channel tactile prosthesis depending on speech signal caracteristics to be perceived. The system is based on three chips: a Codec TCM29C13, a DSP Digital Signal Processor TMS320E17 and a full custom integrated BiCmos circuit. Speech coding is based on LPC coefficients estimation and vector quantization. The architecture of this portable low power unit allows a marked improvement synthetic vowel discrimination.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.832
Threshold uncertainty score0.355

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)0.0000.000

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 head0.041
GPT teacher head0.297
Teacher spread0.256 · 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 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

Citations1
Published2005
Admission routes1
Has abstractyes

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