Using X+V to construct a non-proprietary speech browser for a public-domain SpeechWeb
Bibliographic record
Abstract
A SpeechWeb is a collection of hyperlinked speech applications that are distributed over the Internet. Users access the speech applications through remote browsers, which accept human-voice-input and return synthesized-voice-output. In previous research, a new architecture (LRRP) has been proposed, which is ideally suited for building a Public-Domain SpeechWeb. However, a non-proprietary speech browser is needed for this architecture. In this thesis, we have solved several limitations of X+V, a programming language for developing Multimodal applications, and we have used X+V to build a viable Public-Domain SpeechWeb browser. Our browser has the following properties: real-time human-machine speech interaction; ease of installation and use; acceptable speech-recognition accuracy in a suitable environment; no cost, non-proprietary, ease of distribution; use of common communication protocol---CGI; ease of creation of speech applications; possibility to deploy on mobile devices.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .M31. Source: Masters Abstracts International, Volume: 45-01, page: 0360. Thesis (M.Sc.)--University of Windsor (Canada), 2006.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".