TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
Speech Recognition	TUDA	Kaldi	Test WER	20.5%	# 8
Speech Recognition	TUDA	PocketSphinx	Test WER	39.6%	# 9

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Open Source German Distant Speech Recognition: Corpus and Acoustic Model

International Conference on Text, Speech, and Dialogue 2015 · Stephan Radeck-Arneth, Benjamin Milde, Arvid Lange, Evandro Gouvea, Stefan Radomski, Max Mühlhäuser, and Chris Biemann ·

We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus. The corpus has been recorded in a controlled environment with three different microphones at a distance of one meter. It comprises 180 different speakers with a total of 36 hours of audio recordings. We show recognition results with the open source toolkit Kaldi (20.5% WER) and PocketSphinx (39.6% WER) and make a complete open source solution for German distant speech recognition possible.

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