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

Subject Areas : Intelligent Multimedia Processing and Communication Systems (IMPCS)

Mohammad  Manthouri ¹ (Department of Electrical and Electronic Engineering, Shahed University, Tehran, Iran)
Soheil  Tehranipour ² (Department of Computer Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran)
Samaneh  Yazdani ³ (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. Our innovations in this article will be as follows: • Using transfer learning, we presented a monolingual Persian speech recognition system to recognize Iranian cities. • Compared to other architectures developed for the Persian language, it performs better. • Considering our goal for operational use in call centers, ParsAirCall is optimized in terms of hardware resource consumption and processing load. • The final ParsAirCall solution has been implemented and optimized in Farsi language for use in call centers and conversational artificial intelligence.