Update README.md

README.md

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 # Filipino-English Code-Switching Speech Dataset
 
-**Status: Under publication**
-
 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**.
 
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@@ -14,12 +12,6 @@ A **Filipino-English (Taglish) read-speech dataset** created for training and ev
 | **Train / Test** | 6.89 h / 2.04 h     |
 | **Split**        | Speaker-disjoint    |
 
-## Collection & Quality Control
-
-Reading prompts were drawn from Philippine news text and filtered specifically for **code-switching, readability, and appropriateness**. Recordings were collected through a web-based platform and reviewed by native Filipino-speaking annotators.
-
-Transcripts were manually normalized, including abbreviation expansion, number normalization, initialism handling, and correction of audio–text mismatches. Annotation disagreements were resolved through expert review. The dataset also provides **signal-to-noise ratio (SNR)** estimates for optional quality filtering.
-
 ## ASR Results
 
 The dataset was evaluated using several pretrained ASR models.
@@ -31,10 +23,12 @@ The dataset was evaluated using several pretrained ASR models.
 | Qwen3-ASR            |         25.17% |        **20.13%** |
 | Gemma 4              |         16.18% |                 — |
 
-Fine-tuning on the dataset produced relative WER reductions of **32.16% for Whisper, 33.36% for MMS, and 20.02% for Qwen3-ASR**. In contrast, fine-tuning on general Filipino FLEURS data did not improve performance on the Taglish test set, highlighting the value of **domain-matched code-switched speech data**.
+Fine-tuning on the dataset produced relative WER reductions of **32.16% for Whisper, 33.36% for MMS, and 20.02% for Qwen3-ASR**. In contrast, fine-tuning on general Filipino FLEURS data did not improve performance on the Taglish test set, highlighting the value of domain-matched code-switched speech data.
+
+The evaluation script can be found at ./eval_script.py
 
-## Intended Use & Limitations
+## 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. Final repository, license, and citation details will be provided upon release.**
+**The dataset and accompanying study are currently under publication. Final repository, license, and citation details will be provided upon release.**