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.

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Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis.

This paper reviews the applied Deep Learning (DL) practices in the field of Speaker Recognition (SR), both in verification and identification. Speaker Recognition has been a widely used topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5–6 years. However, as Deep Learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in Speaker Recognition too. It seems that Deep Learning becomes the now state-of-the-art solution for both Speaker Verification (SV) and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to Deep Learning, where they are the most effective.

For the automatic punctuation of Automatic Speech Recognition (ASR) output, both prosodic and text based features are used, often in combination. Pure prosody based approaches usually have low computation needs, introduce little latency (delay) and they are also more robust to ASR errors. Text based approaches usually yield better performance, they are however resource demanding (both regarding their training and computational needs), often introduce high time latency and are more sensitive to ASR errors. The present paper proposes a lightweight prosody based punctuation approach following a new paradigm: we argue in favour of an all-inclusive modelling of speech prosody instead of just relying on distinct acoustic markers: first, the entire phonological phrase structure is reconstructed, then its close correlation with punctuations is exploited in a sequence modelling approach with recurrent neural networks. With this tiny and easy to implement model we reach performance in Hungarian punctuation comparable to large, text based models for other languages by keeping resource requirements minimal and suitable for real-time operation with low latency.