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For noisy speech experiments, we use the CU-Move extended digits corpus [30] which was collected in real car environments.
We address issues for improving handsfree speech recognition performance in different car environments using a single distant microphone.
SpeechDat-Car [106], which comprises a collection of speech resources to support training and testing of multilingual speech recognition applications in car environments (18 7 h in total).
The combination of Wi-Fi hotspot and autonomous vehicles (let alone consumer/business choice of on-demand vehicles) will propel instant conversion/personalization of smart car environments to passenger preferences.
Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5 and 14.8, compared to original noisy speech and ETSI advanced front end, respectively.
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It's got to be meaningful to that person and relevant when you're in a car environment.
To motivate this approach, we first discuss some measurement results obtained in a car environment.
In the first example, the measurements were performed in a car environment.
We consider a noisy car environment as well as a reverberant classroom.
The upper figure presents the measured transfer characteristics for two microphones in a car environment.
The paper is organized as follows: In Section 2, we present some measurement results obtained in a car environment.
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