LRRP SpeechWebs
Bibliographic record
Abstract
The paper describes a new architecture for accessing hyperlinked speech-accessible knowledge sources that are distributed over the Internet. The architecture, LRRP SpeechWeb, uses Local thin-client application-specific speech Recognition and Remote natural-language query Processing. Users navigate an LRRP SpeechWeb using voice-activated hyperlink commands, and query the knowledge sources through spoken natural-language using a speech browser executing on a local device (Frost, R.A. and Chitte, S., Proc. PACLING '99, Conf. of Pacific Association for Computational Linguistics, p.82-90, 1999). It differs from the use of speech interfaces to conventional Web HTML pages, from conventional telephone access to remote speech applications (as used in many call centers), and from the use of a network of hyperlinked VXML pages. The architecture is ideally suited for use when cell-phones become available with built-in speech-to-text and text-to-speech capabilities.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.100 | 0.095 |
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".