Update README.md

README.md

@@ -1,26 +1,80 @@
----
-license: mit
-dataset_info:
-  features:
-  - name: audio
-    dtype: audio
-  - name: speaker_id
-    dtype: string
-  - name: transcript
-    dtype: string
-  - name: native_language
-    dtype: string
-  - name: subset
-    dtype: string
-  splits:
-  - name: all
-    num_bytes: 3179636720.056
-    num_examples: 41806
-  download_size: 3667597693
-  dataset_size: 3179636720.056
-configs:
-- config_name: default
-  data_files:
-  - split: all
-    path: data/all-*
----
+---
+language:
+- en
+license: cc0-1.0
+task_categories:
+- text-to-audio
+- automatic-speech-recognition
+- audio-to-audio
+- audio-classification
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: speaker_id
+    dtype: string
+  - name: transcript
+    dtype: string
+  - name: native_language
+    dtype: string
+  - name: subset
+    dtype: string
+  splits:
+  - name: all
+    num_bytes: 3179636720.056
+    num_examples: 41806
+  download_size: 3667597693
+  dataset_size: 3179636720.056
+configs:
+- config_name: default
+  data_files:
+  - split: all
+    path: data/all-*
+---
+
+# ESLTTS
+
+The full paper can be accessed here: [arXiv](https://arxiv.org/abs/2404.18094), [IEEE Xplore](https://ieeexplore.ieee.org/document/10508477).
+
+## Dataset Access
+You can access this dataset through [Huggingface](https://huggingface.co/datasets/MushanW/ESLTTS) or [Google Driver](https://drive.google.com/file/d/1ChQ_z-TxvKWNUbUMWnbyjM2VY3v2SKEi/view?usp=sharing) or [IEEE Dataport](http://ieee-dataport.org/documents/english-second-language-tts-esltts-dataset).
+
+## Abstract
+
+With the progress made in speaker-adaptive TTS approaches, advanced approaches have shown a remarkable capacity to reproduce the speaker’s voice in the commonly used TTS datasets. However, mimicking voices characterized by substantial accents, such as non-native English speakers, is still challenging.  Regrettably, the absence of a dedicated TTS dataset for speakers with substantial accents inhibits the research and evaluation of speaker-adaptive TTS models under such conditions. To address this gap, we developed a corpus of non-native speakers' English utterances.
+
+We named this corpus “English as a Second Language TTS dataset ” (ESLTTS). The ESLTTS dataset consists of roughly 37 hours of 42,000 utterances from 134 non-native English speakers. These speakers represent a diversity of linguistic backgrounds spanning 31 native languages. For each speaker, the dataset includes an adaptation set lasting about 5 minutes for speaker adaptation, a test set comprising 10 utterances for speaker-adaptive TTS evaluation, and a development set for further research.
+
+## Dataset Structure
+
+```
+ESLTTS Dataset/
+├─ Malayalam_3/     ------------ {Speaker Native Language}_{Speaker id}
+│  ├─ ada_1.flac    ------------ {Subset Name}_{Utterance id}
+│  ├─ ada_1.txt     ------------ Transcription for "ada_1.flac"
+│  ├─ test_1.flac   ------------ {Subset Name}_{Utterance id}
+│  ├─ test_1.txt    ------------ Transcription for "test_1.flac"
+│  ├─ dev_1.flac    ------------ {Subset Name}_{Utterance id}
+│  ├─ dev_1.txt     ------------ Transcription for "dev_1.flac"
+│  ├─ ...
+├─ Arabic_3/        ------------ {Speaker Native Language}_{Speaker id}
+│  ├─ ada_1.flac    ------------ {Subset Name}_{Utterance id}
+│  ├─ ...
+├─ ...
+```
+
+## Citation
+```
+@article{wang2024usat,
+  author       = {Wenbin Wang and
+                  Yang Song and
+                  Sanjay K. Jha},
+  title        = {{USAT:} {A} Universal Speaker-Adaptive Text-to-Speech Approach},
+  journal      = {{IEEE} {ACM} Trans. Audio Speech Lang. Process.},
+  volume       = {32},
+  pages        = {2590--2604},
+  year         = {2024},
+  url          = {https://doi.org/10.1109/TASLP.2024.3393714},
+  doi          = {10.1109/TASLP.2024.3393714},
+}
+```