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Automatic speech recognition (ASR) technique is becoming increasingly popularto improve the efficiency and safety of air traffic control (ATC) operations.However, the conversation between ATC controllers and pilots using multilingualspeech brings a great challenge to building high-accuracy ASR systems. In thiswork, we present a two-stage multilingual ASR framework. The first stage is totrain a language identifier (LID), that based on a recurrent neural network(RNN) to obtain sentence language identification in the form of one-hotencoding. The second stage aims to train an RNN-based end-to-end multilingualrecognition model that utilizes sentence language features generated by LID toenhance input features. In this work, We introduce Featurewise LinearModulation (FiLM) to improve the performance of multilingual ASR by utilizingsentence language identification. Furthermore, we introduce a new sentencelanguage identification learning module called SLIL, which consists of a FiLMlayer and a Squeeze-and-Excitation Networks layer. Extensive experiments on theATCSpeech dataset show that our proposed method outperforms the baseline model.Compared to the vanilla FiLMed backbone model, the proposed multilingual ASRmodel obtains about 7.50% character error rate relative performanceimprovement.

Enhancing multilingual speech recognition in air traffic control by sentence-level language identification