Datasets:
task_categories:
- automatic-speech-recognition
language:
- tl
tags:
- Audio
FilSwitch: A Filipino–English Code-Switched Speech Dataset
A Filipino-English (Taglish) read-speech dataset created for training and evaluating automatic speech recognition (ASR) systems on code-switched speech. The corpus contains 3,555 manually validated utterances from 152 speakers, totaling approximately 8.9 hours of audio.
| Languages | Filipino + English code switching |
| Domain | Read, Philippine news |
| Utterances | 3,555 (16 kHz sampling rate) |
| Train / Test | 6.89 h / 2.04 h (speaker disjoint) |
ASR Results
| Model | Pretrained WER | FilSwitch Fine-tuning | FLEURS Fine-tuning |
|---|---|---|---|
| Whisper Large v3 | 13.62% | 9.24% | 14.71% |
| MMS | 26.41% | 17.60% | 27.36% |
| Qwen3-ASR | 25.17% | 20.13% | 26.45% |
| Gemma 4 E4B | 16.18% | — | — |
Fine-tuning on FilSwitch produced relative WER reductions of 32.16% for Whisper, 33.36% for MMS, and 20.02% for Qwen3-ASR. In contrast, fine-tuning on Filipino FLEURS did not result in WER reductions on the FilSwitch test set.
The evaluation and text normalization script can be found at ./eval_script.py.
Intended Use
The dataset is intended for Filipino-English ASR fine-tuning, benchmarking, and error analysis.
The dataset and accompanying study are currently under publication. Please contact sqwerttyuiiop<at>gmail.com for collection methodology (paper). If you found this work useful, please consider citing it.