Open Source German Distant Speech Recognition: Corpus and Acoustic Model

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Speech Recognition TUDA Kaldi Test WER 20.5% # 8
Speech Recognition TUDA PocketSphinx Test WER 39.6% # 9


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