Improving air traffic control speech intelligibility by reducing speaking rate effectively

Low intelligibility of Air Traffic Control (ATC) speech is one major cause of aircraft accidents every year. Many factors can affect speech intelligibility, among which the most prominent aspects is the high speaking rate commonly present in ATC speech. Hence, a possible solution would be to improve intelligibility by artificially lengthening the spoken utterance to lower the speaking rate. In this work, we explore the lengthening of clean recorded ATC utterances by first identifying phoneme sequences in a given utterance. Such identified phoneme segments can then be lengthened. We will examine effects of lengthening vowels-only, consonants-only, or homogeneous lengthening. To verify our approach, we will conduct human listening test to evaluate the intelligibility. The results show 74.67% was obtained in AB preference test.Accepted versio

A MACHINE LEARNING FRAMEWORK FOR AUTOMATIC SPEECH RECOGNITION IN AIR TRAFFIC CONTROL USING WORD LEVEL BINARY CLASSIFICATION AND TRANSCRIPTION

Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to solve these problems by presenting a machine learning framework which uses word-by-word audio samples to transcribe ATC speech. Instead of transcribing an entire speech sample, this framework transcribes every word individually. Then, overall transcription is pieced together based on the word sequence. Each stage of the framework is trained and tested independently of one another, and the overall performance is gauged. The overall framework was gauged to be a feasible approach to ASR in ATC