Establishing a methodology for benchmarking speech synthesis for computer-assisted language learning (CALL)
Why this work is in the frame
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Bibliographic record
Abstract
Despite the new possibilities that speech synthesis brings about, few Computer-Assisted Language Learning (CALL) applications integrating speech synthesis have found their way onto the market.One potential reason is that the suitability and benefits of the use of speech synthesis in CALL have not been proven.One way to do this is through evaluation.Yet, very few formal evaluations of speech synthesis for CALL purposes have been conducted.One possible reason for the neglect of evaluation in this context is the fact that it is expensive in terms of time and resources.An important concern given that there are several levels of evaluation from which such applications would benefit.Benchmarking, the comparison of the score obtained by a system with that obtained by one which is known, to guarantee user satisfaction in a standard task or set of tasks, is introduced as a potential solution to this problem.In this article, we report on our progress towards the development of one of these benchmarks, namely a benchmark for determining the adequacy of speech synthesis systems for use in CALL.We do so by presenting the results of a case study which aimed to identify the criteria which determine the adequacy of the output of speech synthesis systems for use in its various roles in CALL with a view to the selection of benchmark tests which will address these criteria.These roles (reading machine, pronunciation model, and conversational partner) are also discussed here.An agenda for further research and evaluation is proposed in the conclusion.One possible reason for this is the fact that the suitability and benefits of the use of TTS in CALL have not been proven.One way in which this can be achieved is through evaluation.Ideally, CALL applications integrating TTS would benefit from six stages of evaluation.The objects of these six stages of evaluation are:1) the viability and potential benefits of the use of TTS in CALL, 2) the adequacy of TTS for use in CALL,
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it