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@article{151741,
        author = {Abhinav Parag Mishra and Rishabh Shukla and Manish Pandey and Gyanendra Nath Dwivedi},
        title = {Conversational Interactive Voice Response System with few-shot intent classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {1},
        pages = {719-722},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151741},
        abstract = {Interactive Voice Response (IVR) systems have been helping businesses in providing support to their customers over a telephone network. Modern IVRs not only have speech recognition facility, but also have an AI-powered conversational system with intent classifier. Such IVRs improve user experience, but building such a system is resource-intensive and labor-intensive process. Resources are required to fine-tune and train the intent classifier every time an intent is added. The labor-intensive part of the process is creating a domain specific labelled dataset for training the intent classifier. In this paper, we propose a smart IVR system with intent classifier based on dual sentence encoders. The intent classifier used not only requires very less resources for training, but also works better than models like fine-tuned BERT in few-shot setups (when examples per intent are less). Cost of building conversational IVR is brought down even more when such intent classifier is built in conjunction with open-source technologies like Asterik (for voice server) and Festival (for text-to-speech conversion).},
        keywords = {Dual sentence encoder, Intent classifier, Interactive Voice Response (IVR), VoiceXML},
        month = {},
        }