ParsAirCall: Automated Conversational IVR in Airport Call Center using Deep Transfer Learning

Subject Areas : Multimedia Processing, Communications Systems, Intelligent Systems

Soheil Tehranipour ¹ , Mohammad Manthouri ² , Samaneh Yazdani ³

1 - MS Student, Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Electrical and Electronic Engineering, Shahed University, Tehran, Iran
3 - Assistant Professor, Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2023-04-05 Accepted : 2022-05-17 Published : 2023-06-22

Keywords: Call center, Automatic speech recognition, Deep transfer learning, Airport smart systems,

Abstract :

Introduction: In this paper, we introduce the ParsAirCall toolkit, which is a tool for automatic recognition of Persian numbers in airport systems. It leverages deep transfer learning to improve performance in real and operational scenarios of voice-controlled smart telephone systems at airports across the country. In today's world, with the advancements in artificial intelligence, traditional systems for interacting with callers in telephone calls are not efficient, and this efficiency will be enhanced through automation and the automation of repetitive tasks.

Method: ParsAirCall distinguishes itself by surpassing competing models in the Persian language, achieving heightened accuracy with fewer parameters and optimized computing resources. Addressing the challenge posed by limited data for Persian speech recognition, we meticulously curated a 30-hour telephony dataset, serving as the cornerstone for training the final ParsAirCall model. Embracing the innovative QuartzNet architecture, our deep transfer learning strategy empowers ParsAirCall to capture nuanced features in Persian speech, ensuring superior performance in number recognition tasks associated with airport telephone calls.

Results: Experiments were conducted on both our collected telephony dataset and the Common Voice project, demonstrating ParsAirCall’s efficiency in achieving a 2.7% WER (Word Error Rate) in number recognition in airport telephone calls.

Discussion: ParsAirCall emerges as a versatile tool, poised for seamless integration as a service into any Persian-language airport telephone system. Its practical application extends to number recognition in airport call centers, exemplifying the transformative impact of advanced technologies in streamlining communication processes within critical operational environments. ParsAirCall can be easily integrated as a service into any Persian-language airport telephone system, making it a practical tool for number recognition in airport call centers and telephone systems.

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