Using linguistic knowledge for improving automatic speech recognition accuracy in air traffic control
Recently, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC) such as air traffic control simulation and training, monitoring live operators for with the aim of safety improvements, air traffic controller workload measurement and conducting analysis on large quantities controllerpilot speech. However, due to the high accuracy requirements of the ATC context and its unique challenges such as call sign detection, the problem of poor input signal quality, the problem of ambiguity, the use of non standard phraseology and the problem of dialects, accents and multiple languages, ASR has not been widely adopted in this field. In this thesis, in order to take advantage of the availability of linguistic knowledge, particularly syntactic and semantic knowledge, in the ATC domain, I aim at using different levels of linguistic knowledge to improve the accuracy of ASR systems via three steps: language modeling, n best list re ranking using syntactic knowledge and n best list re ranking using semantic knowledge. Firstly, I propose a context dependent class n gram language model by combining the hybrid class n gram and context dependent language modeling approaches to address the two main challenges of language modeling in ATC, which are the lack of ATC related corpora for training and the location based data problem. Secondly, I use the first level of linguistic knowledge, syntactic knowledge to perform n best list re ranking. To facilitate this, I propose a novel feature called syntactic score and a WER Sensitive Pairwise Perceptron algorithm. I use the perceptron algorithm to combine the proposed feature with the speech decoder's confidence score feature to re rank the n best list. Thirdly, I combine syntactic knowledge with the next level of linguistc knowledge, semantic knowledge to re rank the n best list. To do this, I propose a feature called semantic relatedness. I use the WER Sensitive Pairwise Perceptron algorithm to combine the proposed feature with the syntactic score and speech decoder's confidence score features perform n best list re ranking. Finally, I build a baseline ASR system based on the Pocketsphinx recognizer from the CMU Sphinx framework, the CMUSphinx US English generic acoustic model and the generic cmudict SPHINX 40 pronunciation dictionary and the three above mentioned approaches. I evaluate the baseline ASR system in terms of Word Error Rate (WER) on the well known ATCOSIM Corpus of Non prompted Clean Air Traffic Control Speech (ATCOSIM) and my own Air Traffic Control Speech Corpus (ATCSC) The evaluation results show that the combination of the three proposed approaches reduces the WER of the baseline ASR system by 20.95% compared with traditional n gram language models in recognizing general clearances from the ATCSC corpus.