Datasets:

iNLP-Lab
/

GlobeAudio

@@ -75,7 +75,7 @@ configs:
 GlobeAudio is a multilingual, multicultural in-the-Wild benchmark for assessing compound audio understanding, comprising of 5,637 carefully constructed MCQs across six typologically diverse languages, namely English (United States), Chinese (China), Thai (Thailand), Russian (Russia), Bengali (India) and Singlish (Singapore). The dataset comprises of naturally occurring audio clips from online media that reflect real-life speech across diverse contexts, and are curated to rigorously examine the ability of multilingual systems to handle acoustic and sociolinguistic variability.
 ## Language Selection
-The 6 carefully selected languages span multiple language families (Indo-European, Sino-Tibetan, Tai-Kadai and creole varieties), covers a wide range of geographic regions, and reflects substantial variation in resource availability. They also differ markedly in script systems and spoken characteristics -- the inclusion of Singlish enables the evaluation of naturally occuring code-mixing and multilingual speech within a single variety, capturing linguistic phenomena that are common in real-world audio but rarely represented in existing benchmarks.
 ## Dataset Quality
 For all selected languages, data annotation is performed exclusively by native speakers for whom the language is their first language, in order to ensure high linguistic fidelity, culturally appropriate interpretation and reliable construction of questions and distractors. Consequently, a two-stage quality control process was conducted involving random sampling and cross-checking, producing a final dataset with an inter-annotator agreement of 95.5%, together with verified and consistent gold labels.

 GlobeAudio is a multilingual, multicultural in-the-Wild benchmark for assessing compound audio understanding, comprising of 5,637 carefully constructed MCQs across six typologically diverse languages, namely English (United States), Chinese (China), Thai (Thailand), Russian (Russia), Bengali (India) and Singlish (Singapore). The dataset comprises of naturally occurring audio clips from online media that reflect real-life speech across diverse contexts, and are curated to rigorously examine the ability of multilingual systems to handle acoustic and sociolinguistic variability.
 ## Language Selection
+The 6 carefully selected languages span multiple language families (Indo-European, Sino-Tibetan, Tai-Kadai and creole varieties), covers a wide range of geographic regions, and reflects substantial variation in resource availability and phonological characteristics. They also differ markedly in script systems and spoken characteristics -- the inclusion of Singlish enables the evaluation of naturally occuring code-mixing and multilingual speech within a single variety, capturing linguistic phenomena that are common in real-world audio but rarely represented in existing benchmarks.
 ## Dataset Quality
 For all selected languages, data annotation is performed exclusively by native speakers for whom the language is their first language, in order to ensure high linguistic fidelity, culturally appropriate interpretation and reliable construction of questions and distractors. Consequently, a two-stage quality control process was conducted involving random sampling and cross-checking, producing a final dataset with an inter-annotator agreement of 95.5%, together with verified and consistent gold labels.