We present our findings from the first shared task on Multi-task Learning in Dravidian Languages at the second Workshop on Speech and Language Technologies for Dravidian Languages. In this task, a sentence in any of three Dravidian Languages is required to be classified into two closely related tasks namely Sentiment Analyis (SA) and Offensive Language Identification (OLI). The task spans over three Dravidian Languages, namely, Kannada, Malayalam, and Tamil. It is one of the first shared tasks that focuses on Multi-task Learning for closely related tasks, especially for a very low-resourced language family such as the Dravidian language family. In total, 55 people signed up to participate in the task, and due to the intricate nature of the task, especially in its first iteration, 3 submissions have been received.
This paper illustrates the overview of the shared task on automatic speech recognition in the Tamil language. In the shared task, spontaneous Tamil speech data gathered from elderly and transgender people was given for recognition and evaluation. These utterances were collected from people when they communicated in the public locations such as hospitals, markets, vegetable shop, etc. The speech corpus includes utterances of male, female, and transgender and was split into training and testing data. The given task was evaluated using WER (Word Error Rate). The participants used the transformer-based model for automatic speech recognition. Different results using different pre-trained transformer models are discussed in this overview paper.
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