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Towards Building a Language-Independent Speech Scoring Assessment
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Builds a language-independent pronunciation and fluency scoring system from XLSR wav2vec 2.0 features and CTC-segmentation force-alignment features, trained on other languages and tested on unseen target languages.
Problem
Automatic speech scoring for language learning needs human-labeled expert ratings per language, which are scarce for non-English languages. The paper asks whether pronunciation and fluency can be scored in a target language without labeled data in that language.
Approach
Uses XLSR-based phoneme posteriors and CTC-segmentation forced alignment, plus ASR error-rate features, with language-specific feature normalization on unlabeled data, to train regression models that transfer across languages. Features are hand-tagged as pronunciation or fluency to reduce spurious cross-competency signal.
Key results
- Language-Independent models reach mean r above 0.65 for pronunciation and above 0.63 for fluency across held-out languages, versus about 0.70 pronunciation for Language-Specific models.
- Adding Spanish to training raised Portuguese fluency correlation by about 30% (from 0.49 to 0.64 with Linear vs SVM comparison in Table 5).
- Inter-rater agreement was r above 0.8 for both pronunciation and fluency.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Spanish to English and French training languages (ablation) | Language-Independent scoring model · Portuguese fluency | Pearson r: 0.49 -> 0.64; increase of 30% | Helped |
| Add Spanish to English and French training languages (ablation) | Language-Independent scoring model · Portuguese pronunciation | Pearson r: 0.78 -> 0.77 | No clear effect |
| Add Spanish to English and French training languages (ablation) | Language-Independent scoring model · German pronunciation | Pearson r: 0.93 -> 0.92 | No clear effect |
| Add Spanish to English and French training languages (ablation) | Language-Independent scoring model · German fluency | Pearson r: 0.79 -> 0.83; only a 5% drop in fluency prediction | Helped |
| Use Language-Independent rather than Language-Specific modeling | Speech scoring models · Portuguese pronunciation | Pearson r: 0.84 -> 0.77 | Hurt |
| Use Language-Independent rather than Language-Specific modeling | Speech scoring models · English pronunciation | Pearson r: 0.70 -> 0.65 | Hurt |
Surprising findings
- Adding the related language Spanish helped fluency prediction for Portuguese but not pronunciation prediction.
- Cross-correlations show predicted pronunciation and fluency scores correlate more with actual ratings of their own competency, but expert ratings of the two competencies are highly correlated.
Limitations
- Language-Independent models underperform Language-Specific and Language-Combined models, especially on fluency MAE.
- Evaluated only on five European languages; scaling to Asian and tonal languages left to future work.
- Dataset is proprietary and not released; bias across demographics not studied.
Experimental setup
Datasets: Common Voice proprietary SHL speech dataset (Read & Speak, Listen & Repeat) with expert pronunciation and fluency ratings
Benchmarks: proprietary SHL speech dataset (5 languages)
Metrics: Pearson correlation (r) mean absolute error (MAE)
Baselines: Language-Specific models Language-Combined model
Builds on: wav2vec 2.0 CTC-Segmentation XLSR
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- Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks (ICLR 2025 · similarity 0.83)
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