A survey and benchmark of Farsi subjective NLP finds few public datasets, missing demographic labels, and highly variable LLM performance across datasets.
A Persian ASR-based SER: Modification of Sharif Emotional Speech Database and Investigation of Persian Text Corpora
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abstract
Speech Emotion Recognition (SER) is one of the essential perceptual methods of humans in understanding the situation and how to interact with others, therefore, in recent years, it has been tried to add the ability to recognize emotions to human-machine communication systems. Since the SER process relies on labeled data, databases are essential for it. Incomplete, low-quality or defective data may lead to inaccurate predictions. In this paper, we fixed the inconsistencies in Sharif Emotional Speech Database (ShEMO), as a Persian database, by using an Automatic Speech Recognition (ASR) system and investigating the effect of Farsi language models obtained from accessible Persian text corpora. We also introduced a Persian/Farsi ASR-based SER system that uses linguistic features of the ASR outputs and Deep Learning-based models.
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Exploring Subjective Tasks in Farsi: A Survey Analysis and Evaluation of Language Models
A survey and benchmark of Farsi subjective NLP finds few public datasets, missing demographic labels, and highly variable LLM performance across datasets.