Adapting automatic speech recognition methods to speech perception: A hidden semi-Markov model of listener’s categorization of a VC(C)V continuum
Bibliographic record
Abstract
This study reanalyzes the categorization of 144 synthetic VC(C)V stimuli by 13 listeners. Medial silent gap duration and frequencies of pre- and postgap F2−F3 transitions were independently varied to cover the response set / aba, abda, ab♯ba, ada, adba, ad♯da/. A simple logistic model was shown to work very well for the complex response patterns [T. Nearey and R. Smits, J. Acoust. Soc. Am. 24, 111, 2434 (2002)]. The former analysis required a static ‘‘spoon-fed’’ description of the variable duration stimuli in terms of synthesis parameters. A new analysis will be presented using a hidden semi-Markov model (HSMM), which is a fully automatic dynamic pattern recognizer. The HSMM includes explicit state durations and is fitted to perceptual data by optimization methods allied to the maximum mutual information (MMI) approach from the automatic speech recognition literature. Given only the waveforms of the stimuli as input, the trained HSMM provides an extremely good fit to the perceptual data. The fully automatic HSMM performs better than the spoon-fed static logistic model, with rms error values of 4.8 versus 5.9 percent, respectively. The flexibility and generality of the HSMM/MMI framework for perception models will be sketched. [Work supported by SSHRC.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".