Preprint
Realistic multi-microphone data simulation for distant speech recognition
Mirco Ravanelli et al., arXiv (preprint, eess.AS), 2017
Read the originalRead on October 16, 2026
- What it is
- A 2017 preprint from Fondazione Bruno Kessler that compares speech recognition on close-talking recordings with distant recordings made by microphones in a real apartment. It reports a 3.7% word error rate for close-talking speech and a 12.0% word error rate for a single ceiling microphone with DNN models.
- How much weight it carries
- A preprint (version 1), so it has not been through the journal review process. The test uses read Wall Street Journal sentences, a DNN recognizer from 2017, and one room, so the numbers show the size of the gap rather than a general rule for every laptop.
Facts taken from this source
- Decoding close-talking Wall Street Journal sentences recorded in a studio gave a word error rate of 3.7%, using DNN models trained on clean WSJ data.
“is 3.7% (using DNN models trained with the original clean WSJ data set).” (Section 4.1, Close-talking baseline)
- The authors state that for distant speech the performance of automatic speech recognition is dramatically reduced compared with the close-talking case.
“in the case of distant-speech input the ASR performance is dramatically reduced, if compared to a close-talking case.” (Section 4.2, Single distant-microphone performance)
- With a single distant ceiling microphone in an apartment and real recordings, the DNN recognizer's word error rate was 12.0%.
“DNN 12.0 13.2 10.7 11.6 7.2 7.6” (Table 1 (DNN row, first column: single distant microphone, real data))
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