Update README.md

README.md

@@ -182,7 +182,7 @@ CAMEO contains audio and transcription in eight languages: Bengali, English, Fre
 ### Data Fields
 
 - `file_id` (`str`): A unique identifier of the audio sample.
-- `audio` (`dict`): A dictionary containing the file path to the audio sample, the raw waveform as a one-dimensional NumPy array, and the sampling rate (16 kHz).
+- `audio` (`dict`): A dictionary containing the file path to the audio sample, the raw waveform, and the sampling rate (16 kHz).
 - `emotion` (`str`): A label indicating the expressed emotional state.
 - `transcription` (`str`): The orthographic transcription of the utterance.
 - `speaker_id` (`str`): A unique identifier of the speaker.
@@ -194,38 +194,238 @@ CAMEO contains audio and transcription in eight languages: Bengali, English, Fre
 
 ## Data Splits
 
-The entire dataset is labeled as a test set and is not divided into separate train and test splits. As all the selected corpora are publicly available, there is a potential risk of their usage in model training. This setup provides researchers and developers with a benchmark for evaluating cross-lingual model performance.
-
-### Datasets Information
-| Dataset | Language | Samples | Emotions |
-|---------|----------|---------|---------|
-| CaFE  | French   | 936     | anger, disgust, fear, happiness, neutral, sadness, surprise |
-| CREMA-D | English | 7442   | anger, disgust, fear, happiness, neutral, sadness |
-| EMNS  | English  | 1205    | anger, disgust, excitement, happiness, neutral, sadness, sarcasm, surprise |
-| Emozionalmente | Italian | 6902 |anger, disgust, fear, happiness, neutral, sadness, surprise |
-| eNTERFACE | English | 1257 | anger, disgust, fear, happiness, sadness, surprise |
-| JL-Corpus | English | 2400 | anger, anxiety, apology, assertiveness, concern, encouragement, excitement, happiness, neutral, sadness |
-| MESD  | Spanish  | 862       |anger, disgust, fear, happiness, neutral, sadness |
-| nEMO  | Polish   | 4481    | anger, fear, happiness, neutral, sadness, surprise |
-| Oréau | French   | 502     | anger, disgust, fear, happiness, neutral, sadness, surprise |
-| PAVOQUE | German | 5442    | anger, happiness, neutral, poker, sadness |
-| RAVDESS | English | 1440   | anger, calm, disgust, fear, happiness, neutral, sadness, surprise |
-| RESD  | Russian  | 1396    | anger, disgust, enthusiasm, fear, happiness, neutral, sadness |
-| SUBESCO | Bengali | 7000   | anger, disgust, fear, happiness, neutral, sadness, surprise |
+Since all corpora are already publicly available, there is a risk of contamination. Because of that, **CAMEO** is not divided into train and test splits.
+
+| Split | Dataset | Language | Samples | Emotions |
+| ----- |---------|----------|---------|---------|
+| `cafe` | CaFE  | French   | 936     | anger, disgust, fear, happiness, neutral, sadness, surprise |
+| `crema_d` | CREMA-D | English | 7442   | anger, disgust, fear, happiness, neutral, sadness |
+| `emns` | EMNS  | English  | 1205    | anger, disgust, excitement, happiness, neutral, sadness, sarcasm, surprise |
+| `emozionalmente` | Emozionalmente | Italian | 6902 |anger, disgust, fear, happiness, neutral, sadness, surprise |
+| `enterface` | eNTERFACE | English | 1257 | anger, disgust, fear, happiness, sadness, surprise |
+| `jl_corpus` | JL-Corpus | English | 2400 | anger, anxiety, apology, assertiveness, concern, encouragement, excitement, happiness, neutral, sadness |
+| `mesd` | MESD  | Spanish  | 862       |anger, disgust, fear, happiness, neutral, sadness |
+| `nemo` | nEMO  | Polish   | 4481    | anger, fear, happiness, neutral, sadness, surprise |
+| `oreau` | Oréau | French   | 502     | anger, disgust, fear, happiness, neutral, sadness, surprise |
+| `pavoque` | PAVOQUE | German | 5442    | anger, happiness, neutral, poker, sadness |
+| `ravdess` | RAVDESS | English | 1440   | anger, calm, disgust, fear, happiness, neutral, sadness, surprise |
+| `resd` | RESD  | Russian  | 1396    | anger, disgust, enthusiasm, fear, happiness, neutral, sadness |
+| `subesco` | SUBESCO | Bengali | 7000   | anger, disgust, fear, happiness, neutral, sadness, surprise |
+
+## Dataset Creation
+
+The inclusion of a dataset in the collection was determined by the following criteria:
+- The corpus is publicly available and distributed under a license that allows free use for non-commercial purposes and creation of derivative works.
+- The dataset includes transcription of the speech, either directly within the dataset, associated publications or documentation.
+- The annotations corresponding to basic emotional states are included and consistent with commonly used naming conventions.
+- The availability of speaker-related metadata (e.g., speaker identifiers or demographic information) was considered valuable, but not mandatory.
 
 ## Additional Information
 
 ### Licensing Information
-The licence of each dataset is described in the `license` field in the metadata.
+
+The **CAMEO** collection is available under CC BY-NC-SA 4.0 license.
+
+The datasets used for the creation of **CAMEO** have specific licensing terms that must be understood and agreed beforeuse.
+The following licenses apply to the corpora:
+- CC BY-NC-SA 4.0 applies to CaFE, nEMO, PAVOQUE, RAVDESS,
+- Open Database License applies to CREMA-D,
+- Apache 2.0 applies to EMNS,
+- CC BY 4.0 applies to Emozionalmente, MESD, Oréau, SUBESCO,
+- MIT applies to eNTERFACE, RESD,
+- CC0: Public Domain applies to JL-Corpus.
+
+Additionally, the licence of each dataset is described in the `license` field in the metadata.
+
+### Contributions
+
+Thanks to [@iwonachristop](https://huggingface.co/iwonachristop) and [@MaciejCzajka](https://huggingface.co/MaciejCzajka) for adding this dataset.
 
 ### Citation Information
 
 You can access the CAMEO paper at [arXiv](). When referencing the **CAMEO** collection, please cite the paper as follows, along with the original datasets incuded in the corpus.
 
 ```
+@mis{cameo}
 
-```
+@inproceedings{cafe,
+  author = {Gournay, Philippe and Lahaie, Olivier and Lefebvre, Roch},
+  title = {{A Canadian French Emotional Speech Dataset}},
+  year = {2018},
+  isbn = {9781450351928},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3204949.3208121},
+  doi = {10.1145/3204949.3208121},
+  booktitle = {Proceedings of the 9th ACM Multimedia Systems Conference},
+  pages = {399–402},
+  numpages = {4},
+  keywords = {canadian french, digital recording, emotional speech, speech dataset},
+  location = {Amsterdam, Netherlands},
+  series = {MMSys '18}
+}
 
-### Contributions
+@article{cremad,
+  author = {Cao, Houwei and Cooper, David and Keutmann, Michael and Gur, Ruben and Nenkova, Ani and Verma, Ragini},
+  year = {2014},
+  month = {10},
+  pages = {377-390},
+  title = {{CREMA-D: Crowd-sourced emotional multimodal actors dataset}},
+  volume = {5},
+  journal = {IEEE transactions on affective computing},
+  doi = {10.1109/TAFFC.2014.2336244}
+}
+
+@misc{emns,
+  title={{EMNS /Imz/ Corpus: An emotive single-speaker dataset for narrative storytelling in games, television and graphic novels}},
+  author={Kari Ali Noriy and Xiaosong Yang and Jian Jun Zhang},
+  year={2023},
+  eprint={2305.13137},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2305.13137},
+}
+
+@article{emozionalmente,
+  author = {Catania, Fabio and Wilke, Jordan and Garzotto, Franca},
+  year = {2025},
+  month = {01},
+  pages = {1-14},
+  title = {{Emozionalmente: A Crowdsourced Corpus of Simulated Emotional Speech in Italian}},
+  volume = {PP},
+  journal = {IEEE Transactions on Audio, Speech and Language Processing},
+  doi = {10.1109/TASLPRO.2025.3540662}
+}
+
+@inproceedings{enterface,
+  author={Martin, O. and Kotsia, I. and Macq, B. and Pitas, I.},
+  booktitle={22nd International Conference on Data Engineering Workshops (ICDEW'06)},
+  title={{The eNTERFACE' 05 Audio-Visual Emotion Database}},
+  year={2006},
+  volume={},
+  number={},
+  pages={8-8},
+  keywords={Audio databases;Image databases;Emotion recognition;Spatial databases;Visual databases;Signal processing algorithms;Protocols;Speech analysis;Humans;Informatics},
+  doi={10.1109/ICDEW.2006.145}
+}
+
+@inproceedings{jlcorpus,
+  author = {James, Jesin and Tian, Li and Watson, Catherine},
+  year = {2018},
+  month = {09},
+  pages = {2768-2772},
+  title = {{An Open Source Emotional Speech Corpus for Human Robot Interaction Applications}},
+  doi = {10.21437/Interspeech.2018-1349}
+}
+
+@inproceedings{mesd,
+  author = {Duville, Mathilde Marie and Alonso-Valerdi, Luz and Ibarra-Zarate, David I.},
+  year = {2021},
+  month = {12},
+  pages = {},
+  title = {{The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning}},
+  volume = {2021},
+  doi = {10.1109/EMBC46164.2021.9629934}
+}
+
+  @article{mesd2,
+  author = {Duville, Mathilde Marie and Alonso-Valerdi, Luz and Ibarra-Zarate, David I.},
+  year = {2021},
+  month = {12},
+  pages = {},
+  title = {{Mexican Emotional Speech Database Based on Semantic, Frequency, Familiarity, Concreteness, and Cultural Shaping of Affective Prosody}},
+  volume = {6},
+  journal = {Data},
+  doi = {10.3390/data6120130}
+}
+
+@inproceedings{christop-2024-nemo,
+  title = "n{EMO}: Dataset of Emotional Speech in {P}olish",
+  author = "Christop, Iwona",
+  editor = "Calzolari, Nicoletta  and
+    Kan, Min-Yen  and
+    Hoste, Veronique  and
+    Lenci, Alessandro  and
+    Sakti, Sakriani  and
+    Xue, Nianwen",
+  booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+  month = may,
+  year = "2024",
+  address = "Torino, Italia",
+  publisher = "ELRA and ICCL",
+  url = "https://aclanthology.org/2024.lrec-main.1059/",
+  pages = "12111--12116",
+  abstract = "Speech emotion recognition has become increasingly important in recent years due to its potential applications in healthcare, customer service, and personalization of dialogue systems. However, a major issue in this field is the lack of datasets that adequately represent basic emotional states across various language families. As datasets covering Slavic languages are rare, there is a need to address this research gap. This paper presents the development of nEMO, a novel corpus of emotional speech in Polish. The dataset comprises over 3 hours of samples recorded with the participation of nine actors portraying six emotional states: anger, fear, happiness, sadness, surprise, and a neutral state. The text material used was carefully selected to represent the phonetics of the Polish language adequately. The corpus is freely available under the terms of a Creative Commons license (CC BY-NC-SA 4.0)."
+}
 
-Thanks to [@iwonachristop](https://huggingface.co/iwonachristop) and [@MaciejCzajka](https://huggingface.co/MaciejCzajka) for adding this dataset.
+@misc{oreau,
+  title = {{French emotional speech database - Or{\'e}au}},
+  author = {Kerkeni, Leila and Cleder, Catherine and Serrestou, Youssef and
+               Raoof, Kosai},
+  abstract = {This document presents the French emotional speech database -
+               Or{\'e}au, recorded in a quiet environment. The database is
+               designed for general study of emotional speech and analysis of
+               emotion characteristics for speech synthesis purposes. It
+               contains 79 utterances which could be used in everyday life in
+               the classroom. Between 10 and 13 utterances were written for
+               each of the 7 emotions in French language by 32 non-professional
+               speakers. 2 versions are available, the first one contains 502
+               sentences. A perception test was performed to evaluate the
+               recognition of emotions and their naturalness. 90\% of
+               utterances (434 utterances) were correctly identified and
+               retained after the test and various analyses, which constitutes
+               the second version of database.},
+  publisher = {Zenodo},
+  year      =  {2020}
+}
+
+@inproceedings{pavoque,
+  author = {Steiner, Ingmar and Schröder, Marc and Klepp, Annette},
+  title = {{The PAVOQUE corpus as a resource for analysis and synthesis of expressive speech}},
+  booktitle = {Phonetik & Phonologie 9. Phonetik & Phonologie (P&P-9), October 11-12, Zurich, Switzerland},
+  year = {2013},
+  month = {10},
+  pages = {83--84},
+  organization = {UZH},
+  publisher = {Peter Lang}
+}
+
+@article{ravdess,
+  doi = {10.1371/journal.pone.0196391},
+  author = {Livingstone, Steven R. AND Russo, Frank A.},
+  journal = {PLOS ONE},
+  publisher = {Public Library of Science},
+  title = {{The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English}},
+  year = {2018},
+  month = {05},
+  volume = {13},
+  url = {https://doi.org/10.1371/journal.pone.0196391},
+  pages = {1-35},
+  abstract = {The RAVDESS is a validated multimodal database of emotional speech and song. The database is gender balanced consisting of 24 professional actors, vocalizing lexically-matched statements in a neutral North American accent. Speech includes calm, happy, sad, angry, fearful, surprise, and disgust expressions, and song contains calm, happy, sad, angry, and fearful emotions. Each expression is produced at two levels of emotional intensity, with an additional neutral expression. All conditions are available in face-and-voice, face-only, and voice-only formats. The set of 7356 recordings were each rated 10 times on emotional validity, intensity, and genuineness. Ratings were provided by 247 individuals who were characteristic of untrained research participants from North America. A further set of 72 participants provided test-retest data. High levels of emotional validity and test-retest intrarater reliability were reported. Corrected accuracy and composite "goodness" measures are presented to assist researchers in the selection of stimuli. All recordings are made freely available under a Creative Commons license and can be downloaded at https://doi.org/10.5281/zenodo.1188976.},
+  number = {5},
+}
+
+@misc{resd,
+  author = {Artem Amentes and Nikita Davidchuk and Ilya Lubenets},
+  title = {{Russian Emotional Speech Dialogs with annotated text}},
+  year = {2022},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Hub},
+  howpublished = {\url{https://huggingface.co/datasets/Aniemore/resd_annotated}},
+}
+
+@article{subesco,
+  doi = {10.1371/journal.pone.0250173},
+  author = {Sultana, Sadia AND Rahman, M. Shahidur AND Selim, M. Reza AND Iqbal, M. Zafar},
+  journal = {PLOS ONE},
+  publisher = {Public Library of Science},
+  title = {{SUST Bangla Emotional Speech Corpus (SUBESCO): An audio-only emotional speech corpus for Bangla}},
+  year = {2021},
+  month = {04},
+  volume = {16},
+  url = {https://doi.org/10.1371/journal.pone.0250173},
+  pages = {1-27},
+  abstract = {SUBESCO is an audio-only emotional speech corpus for Bangla language. The total duration of the corpus is in excess of 7 hours containing 7000 utterances, and it is the largest emotional speech corpus available for this language. Twenty native speakers participated in the gender-balanced set, each recording of 10 sentences simulating seven targeted emotions. Fifty university students participated in the evaluation of this corpus. Each audio clip of this corpus, except those of Disgust emotion, was validated four times by male and female raters. Raw hit rates and unbiased rates were calculated producing scores above chance level of responses. Overall recognition rate was reported to be above 70% for human perception tests. Kappa statistics and intra-class correlation coefficient scores indicated high-level of inter-rater reliability and consistency of this corpus evaluation. SUBESCO is an Open Access database, licensed under Creative Common Attribution 4.0 International, and can be downloaded free of charge from the web link: https://doi.org/10.5281/zenodo.4526477.},
+  number = {4},
+}
+```