SpeakerPool: A remote speech data collection platform
Bibliographic record
Abstract
The collection of speech production data for academic use has traditionally been carried out by recording participants in-person. While certain types of research require traditional data collection methods, many factors, such as distance and world events, make remote collection a valuable alternative for other types of research. Despite the existence of capable technologies and the advantages of remote collection methods, to the authors' knowledge there is no freely accessible, reusable, and research-oriented platform for the remote collection of speech production data. We aim to address this with SpeakerPool: a web application that, in addition to providing an easy-to-use recording interface for participants, has built-in functionalities for the automation of many tasks. Users are able to access SpeakerPool regardless of platform using modern web browsers on both mobile and non-mobile devices. This greatly reduces compatibility limitations and bypasses the need to download software. In the present study, we discuss the general advantages and disadvantages of remote speech data collection and investigate the usefulness of SpeakerPool by collecting and analyzing a pilot data set of Malagasy speech.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 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".