Yeon-Jun Kim


A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Ryan Price | Mahnoosh Mehrabani | Narendra Gupta | Yeon-Jun Kim | Shahab Jalalvand | Minhua Chen | Yanjie Zhao | Srinivas Bangalore
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.


Building Text-To-Speech Voices in the Cloud
Alistair Conkie | Thomas Okken | Yeon-Jun Kim | Giuseppe Di Fabbrizio
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The AT&T VoiceBuilder provides a new tool to researchers and practitioners who want to have their voices synthesized by a high-quality commercial-grade text-to-speech system without the need to install, configure, or manage speech processing software and equipment.It is implemented as a web service on the AT&T Speech Mashup Portal.The system records and validates users' utterances, processes them to build a synthetic voice and provides a web service API to make the voice available to real-time applications through a scalable cloud-based processing platform. All the procedures are automated to avoid human intervention. We present experimental comparisons of voices built using the system.