Automatic Speech Recognition in Taxi Call Service Systems

Abstract

In this research, the application of automatic speech recognition system in taxi call services is investigated. In comparison with traditional query handling systems such as live agents, Interactive Voice Response systems, type-base websites and mobile applications, the newest trend of artificial intelligence - speech recognition can be applied to make conversations in more natural way. For developing, training and testing of the system, Kaldi and CMUSphinx open-source speech recognition tools were utilized. Approximately 4 h of speech data in Azerbaijani have been processed for both tools. Testing has been accomplished in two ways; one of which is recognizing dataset from unknown speakers, and the other one is recognizing shuffled dataset. During these tests, variance and speed were investigated, along with accuracy. Kaldi showed accuracy between 97.3 and 99.6 with variance changing between 0.03 and 4.8. On the other hand, CMUSphinx attained accuracy between 95.6 and 97.8 with variance values of 0.2 and 3.8 in relatively less training time. Accomplished results were compared and used to define appropriate parameters for investigated models.