---
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.**