A deep neural network approach to investigate tone space in languages
Bibliographic record
Abstract
Phonological contrasts are usually signaled by multiple cues, and tonal languages typically involve multiple dimensions to distinguish between tones (e.g., duration, pitch contour, and voice quality, etc.). While the topic has been extensively studied, research has mostly used small datasets. This study employs a deep neural network (DNN) based speech recognizer trained on the AISHELL-1 (Bu et al., 2017) speech corpus (178 hours of read speech) to explore the tone space in Mandarin Chinese. A recent study shows that DNN models learn linguistically-interpretable information to distinguish between vowels (Weber et al., 2016). Specifically, from a low-dimensional Bottleneck layer, the model learns features comparable to F1 and F2. In the current study, we propose a more complicated Long Short-Term Memory (LSTM) model—with a Bottleneck layer implemented in the hidden layers—to account for variable duration, an important cue for tone discrimination. By interpreting the features learned in the Bottleneck layer, we explore what acoustic dimensions are involved in distinguishing tones. The large amount of data from the speech corpus also renders the results more convincing and provides additional insights not possible from studies with more limited data sets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".