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README.md

@@ -1,11 +1,12 @@
 ***Spoken Emotion Recognition Datasets:*** *A collection of datasets (count=40) for the purpose of emotion recognition/detection in speech.
-The table is chronologically ordered and includes a description of the content of each dataset along with the emotions included.*
+The table is chronologically ordered and includes a description of the content of each dataset along with the emotions included.
+The table can be browsed, sorted and searched under https://superkogito.github.io/SER-datasets/*
 
 | <sub>Dataset</sub> | <sub>Year</sub> | <sub>Content</sub> | <sub>Emotions</sub> | <sub>Format</sub> | <sub>Size</sub> | <sub>Language</sub> | <sub>Paper</sub> | <sub>Access</sub> | <sub>License</sub> |
 |---------------------------------------------------------------------------------------------------|-----------------|-------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|---------------------|-------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------|----------------------------------------------------------------------------------------------|
-| <sub>[MESD]</sub> | <sub>2022</sub> | <sub>864 audio files of single-word emotional utterances with Mexican cultural shaping.</sub> | <sub>6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.</sub> | <sub>Audio</sub> | <sub>86 MB</sub> | <sub>Spanish (Mexican)</sub> | <sub>[The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning]</sub> | <sub>Open</sub> | <sub>[CC BY 4.0]</sub> |
+| <sub>[MESD]</sub> | <sub>2022</sub> | <sub>864 audio files of single-word emotional utterances with Mexican cultural shaping.</sub> | <sub>6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.</sub> | <sub>Audio</sub> | <sub>0,097 GB</sub> | <sub>Spanish (Mexican)</sub> | <sub>[The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning]</sub> | <sub>Open</sub> | <sub>[CC BY 4.0]</sub> |
 |<sub>[ASVP-ESD]</sub> | <sub>2021</sub> | <sub>~13285 audio files collected from movies, tv shows and youtube containing speech and non-speech.</sub> | <sub>12 different natural emotions (boredom, neutral, happiness, sadness, anger, fear, surprise, disgust, excitement, pleasure, pain, disappointment) with 2 levels of intensity. </sub> | <sub>Audio</sub> | <sub> 2 GB </sub> | <sub>Chinese, English, French, Russian and others<sub> | <sub>--<sub> | <sub> Open access<sub> | <sub>Unknown</sub> |
-| <sub>[ESD]</sub> | <sub>2021</sub> | <sub>29 hours, 3500 sentences, by 10 native English speakers and 10 native Chinese speakers.</sub> | <sub>5 emotions: angry, happy, neutral, sad, and surprise.</sub> | <sub>Audio, Text</sub> | <sub> 2.4 GB (zip) </sub> | <sub> English, Chinese </sub> | <sub>[Seen And Unseen Emotional Style Transfer For Voice Conversion With A New Emotional Speech Dataset]</sub> | <sub>Open access</sub> | <sub>Available under an Academic License </sub> |
+| <sub>[ESD]</sub> | <sub>2021</sub> | <sub>29 hours, 3500 sentences, by 10 native English speakers and 10 native Chinese speakers.</sub> | <sub>5 emotions: angry, happy, neutral, sad, and surprise.</sub> | <sub>Audio, Text</sub> | <sub> 2.4 GB (zip) </sub> | <sub> Chinese, English </sub> | <sub>[Seen And Unseen Emotional Style Transfer For Voice Conversion With A New Emotional Speech Dataset]</sub> | <sub>Open access</sub> | <sub>Available under an Academic License </sub> |
 | <sub>[MuSe-CAR]</sub> | <sub>2021</sub> | <sub>40 hours, 6,000+ recordings of 25,000+ sentences by 70+ English speakers (see db link for details).</sub> | <sub>continuous emotion dimensions characterized using valence, arousal, and trustworthiness.</sub> | <sub>Audio, Video, Text</sub> | <sub> 15 GB </sub> | <sub> English </sub> | <sub>[The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements]</sub> | <sub>Restricted access</sub> | <sub>Available under an Academic License & Commercial License </sub> |
 | <sub>[MSP-Podcast corpus]</sub> | <sub>2020</sub> | <sub>100 hours by over 100 speakers (see db link for details).</sub> | <sub>This corpus is annotated with emotional labels using attribute-based descriptors (activation, dominance and valence) and categorical labels (anger, happiness, sadness, disgust, surprised, fear, contempt, neutral and other). </sub> | <sub>Audio</sub> | <sub> -- </sub> | <sub> -- </sub> | <sub>[The MSP-Conversation Corpus]</sub> | <sub>Restricted access</sub> | <sub>Available under an Academic License & Commercial License </sub> |
 | <sub>[emotiontts open db]</sub> | <sub>2020</sub> | <sub>Recordings and their associated transcriptions by a diverse group of speakers.</sub> | <sub>4 emotions: general, joy, anger, and sadness.</sub> | <sub>Audio, Text</sub> | <sub>--</sub> | <sub>Korean</sub> | <sub>--</sub> | <sub>Partial open access</sub> | <sub>[CC BY-NC-SA 4.0] </sub> |

src/ser-datasets.csv

@@ -1,7 +1,7 @@
 "Dataset","Year","Content","Emotions","Format","Size","Language","Paper","Access","License"
-"`MESD`_","2022","864 audio files of single-word emotional utterances with Mexican cultural shaping.","6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.","Audio","86 MB","Spanish (Mexican)","`The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning`_","Open","`CC BY 4.0`_"
+"`MESD`_","2022","864 audio files of single-word emotional utterances with Mexican cultural shaping.","6 emotions provides single-word utterances for anger, disgust, fear, happiness, neutral, and sadness.","Audio","0,097 GB","Spanish (Mexican)","`The Mexican Emotional Speech Database (MESD): elaboration and assessment based on machine learning`_","Open","`CC BY 4.0`_"
 "`ASVP-ESD`_","2021","~13285 audio files collected from movies, tv shows and youtube containing speech and non-speech.","12 different natural emotions (boredom, neutral, happiness, sadness, anger, fear, surprise, disgust, excitement, pleasure, pain, disappointment) with 2 levels of intensity.","Audio","2 GB","Chinese, English, French, Russian and others","--","Open","Unknown"
-"`ESD`_","2021","29 hours, 3500 sentences, by 10 native English speakers and 10 native Chinese speakers.","5 emotions: angry, happy, neutral, sad, and surprise.","Audio, Text","2.4 GB (zip)","English, Chinese","`Seen And Unseen Emotional Style Transfer For Voice Conversion With A New Emotional Speech Dataset`_","Open","Academic License"
+"`ESD`_","2021","29 hours, 3500 sentences, by 10 native English speakers and 10 native Chinese speakers.","5 emotions: angry, happy, neutral, sad, and surprise.","Audio, Text","2.4 GB (zip)","Chinese, English","`Seen And Unseen Emotional Style Transfer For Voice Conversion With A New Emotional Speech Dataset`_","Open","Academic License"
 "`MuSe-CAR`_","2021","40 hours, 6,000+ recordings of 25,000+ sentences by 70+ English speakers (see db link for details).","continuous emotion dimensions characterized using valence, arousal, and trustworthiness.","Audio, Video, Text","15 GB","English","`The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements`_","Restricted","Academic License & Commercial License"
 "`MSP-Podcast corpus`_","2020","100 hours by over 100 speakers (see db link for details).","This corpus is annotated with emotional labels using attribute-based descriptors (activation, dominance and valence) and categorical labels (anger, happiness, sadness, disgust, surprised, fear, contempt, neutral and other).","Audio","--","--","`The MSP-Conversation Corpus`_","Restricted","Academic License & Commercial License"
 "`emotiontts open db`_","2020","Recordings and their associated transcriptions by a diverse group of speakers.","4 emotions: general, joy, anger, and sadness.","Audio, Text","--","Korean","--","Partial Open","`CC BY-NC-SA 4.0`_"