Model Card: SpeechAligner Reference Model

May 25, 2026 ยท View on GitHub

This is a lightweight model card for the reference model in burn-speech-training. It is meant to make the current limits clear, not to present the model as production-ready.

Model

  • Name: SpeechAligner
  • Framework: Burn 0.21
  • Default shape: CNN + squeeze-and-excitation blocks + self-attention + CTC head
  • Default size: about 1.7M parameters
  • Quickstart size: about 122K parameters

Intended Use

  • Learning how to wire a speech training loop in Rust with Burn.
  • Inspecting a small CTC-based alignment path.
  • Reusing pieces such as feature extraction, batching, model structure, or experiment logging.
  • Running controlled experiments on pronunciation scoring ideas.

Not Intended For

  • Production ASR.
  • Certified language assessment.
  • Medical, hiring, immigration, or high-stakes scoring.
  • Claims about learner ability without further validation.
  • Real-time inference guarantees.

Training And Evaluation Data

  • Synthetic quickstart data: smoke test only.
  • LibriSpeech: used by the real-data training path.
  • SpeechOcean762: used for pronunciation scoring experiments.
  • CMUdict: used for transcript to phoneme lookup.

See docs/datasets.md for provenance and inclusion notes.

Reported Result

The best reported pronunciation-scoring result in the experiment log is about 0.292 Spearman correlation against SpeechOcean762 word-level human scores.

That number is a limitation marker, not a victory claim. The evidence points to representation and data limits more than another small loss-function tweak.

Known Failure Modes

  • MFCC/log-mel features may be too weak for robust pronunciation ranking.
  • CTC alignment loss can interfere with scoring fine-tuning in this setup.
  • SpeechOcean762 word scores are heavily imbalanced toward perfect scores.
  • Out-of-vocabulary words depend on fallback phoneme handling.
  • GPU features are hardware-dependent and not fully covered by CI.

Reproducibility Notes

For a quick smoke test:

cargo run --example train_small --features ndarray --release

For experiment claims, prefer linking to docs/experiment-log.md and include the dataset split, command, commit, hardware, and metric definition.