Abstract-This article presents a microphone array shape calibration procedure for diffuse noise environments. The procedure estimates intermicrophone distances by fitting the measured noise coherence with its theoretical model, and then estimates the array geometry using classical multi-dimensional scaling. The technique is validated on noise recordings from two office environments.
Automatic Speech Recognition (ASR) can introduce higher levels of automation into Air Traffic Control (ATC), where spoken language is still the predominant form of communication. While ATC uses standard phraseology and a limited vocabulary, we need to adapt the speech recognition systems to local acoustic conditions and vocabularies at each airport to reach optimal performance. Due to continuous operation of ATC systems, a large and increasing amount of untranscribed speech data is available, allowing for semi-supervised learning methods to build and adapt ASR models. In this paper, we first identify the challenges in building ASR systems for specific ATC areas and propose to utilize out-of-domain data to build baseline ASR models. Then we explore different methods of data selection for adapting baseline models by exploiting the continuously increasing untranscribed data. We develop a basic approach capable of exploiting semantic representations of ATC commands. We achieve relative improvement in both word error rate (23.5%) and concept error rates (7%) when adapting ASR models to different ATC conditions in a semi-supervised manner.
Automatic speech recognition from distant microphones is a difficult task because recordings are affected by reverberation and background noise. First, the application of the deep neural network (DNN)/hidden Markov model (HMM) hybrid acoustic models for distant speech recognition task using AMI meeting corpus is investigated. This paper then proposes a feature transformation for removing reverberation and background noise artefacts from bottleneck features using DNN trained to learn the mapping between distant-talking speech features and close-talking speech bottleneck features. Experimental results on AMI meeting corpus reveal that the mismatch between close-talking and distant-talking conditions is largely reduced, with about 16% relative improvement over conventional bottleneck system (trained on close-talking speech). If the feature mapping is applied to close-talking speech, a minor degradation of 4% relative is observed.
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