![]() ![]() “ What could one do with a speech perception model that accurately predicts the individual speech recognition performance of a listener in any listening situation?” While this still remains a philosophical question, with the advances in automatic speech recognition (ASR) and especially speech recognition modeling, it will become more tangible in the future. #Motrix speech to text licenseThis is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Ultimately, it may serve as a tool to use validated prediction models in the design of spaces and devices which take speech communication into account. The proposed representation was found to be a suitable target to compare and validate modeling approaches in ecologically relevant contexts, and should help to explore possible applications of future speech recognition models. Using the spatial speech recognition maps to explore this data set demonstrated the suitability of the approach to observe possibly relevant listener behavior. An exemplary modeling toolchain, that is, a combination of an acoustic model, a hearing device model, and a listener model, was used to generate a data set for demonstration purposes. In this contribution, an interactive, that is, user manipulable, representation of speech recognition performance is proposed and investigated by means of a concrete example, which focuses on the listener’s head orientation and the spatial dimensions – in particular width and depth – of an acoustic scene. This raises an interesting question: What could we do if we had a perfect speech intelligibility model? In a first step, means to explore and interpret the predicted outcomes of large numbers of speech recognition experiments would be helpful, and large amounts of data demand an accessible, that is, easily comprehensible, representation. individual hearing aid fitting, they can already be performed. While such predictions may still not be sufficiently accurate for serious applications, such as, e.g. multi-source dynamic, acoustic environments. Prediction models come closer to considering all required factors simultaneously to predict the individual speech recognition performance in complex, that is, e.g. * Corresponding author: their everyday life, the speech recognition performance of human listeners is influenced by diverse factors, such as the acoustic environment, the talker and listener positions, possibly impaired hearing, and optional hearing devices. Medizinische Physik and Cluster of Excellence Hearing4all, Universität Oldenburg, 26111 Oldenburg, Germany ![]()
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