Robust Self-Training System for Spoken Query Information Retrieval using Pitch Range Variations
Bibliographic record
Abstract
This paper presents an automatic user profile building and training (AUPB&T) system using voice pitch variation for speech recognition engines. The problem with current ASR engines is that their vocabularies are usually only suited for general usage. Another problem with current ASR engines is that there is no easy means for visually challenged users to train the engine to improve its performance. Our proposed solution consists of a system that can accept a user's document and favorite Web pages. These documents can then be parsed and their words added to the ASR engine's lexicon. Next, it uses those documents to start an ASR training session. The training can completed automatically by using a high quality text-to-speech (TTS) natural voice. In order to overcome the problem of the limited number of high quality natural TTS voices available, we propose to integrate voice pitch variation during the training phase of AUPB&T, which can cover a broader range of user variability. The results of our experiments using standard ASR and TTS engines show that the AUPB&T system using pitch variation improved the recognition rate for an unknown beta speaker
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".