Hello Milan Young,
thanks for doing the ASR testing. We would be happy to consider your results to be in included in the Opus characterization draft. Please feel free to post them to the Opus IETF mailing list, too. With best regards, Christian Hoene -- Universität Tübingen, Sand 13, 72076 Tübingen, Germany Tel +49 7071 2970532, Fax +49 7071 5220 http://kn.inf.uni-tuebingen.de/staff/hoene.html Von: [email protected] [mailto:[email protected]] Im Auftrag von Young, Milan Gesendet: Mittwoch, 28. November 2012 12:51 An: [email protected] Betreff: [opus] Opus for ASR - update and questions For the last couple months, Nuance has performed extensive testing on how the Opus codec performs in the speech recognition task. Im hoping to publish a full report in the coming months, but until then all I have is a teaser. Opus performed within about 1% of the WER (Word Error Rate) of unencoded audio. This is compared to about 5% for Speex, which was the previous codec of choice. Well done to you all! As Nuance considers migrating to Opus, wed like to consider the topic of transport. Traditionally weve relied on TCP for reasons of reliability. Opus, with its packet redundancy features, offers an attractive real-time alternative that we will soon be testing. But in order to apply an apples-apples comparison we need to model both data rates and latency in real world scenarios. For UDP, Im assuming that the redundancy feature adds no additional latency. Correct? On the data rate question, I see that the Opusenc tool provides an expec-loss parameter with the value expressed as a percentage. Could someone please describe how this is implemented? Are you simply removing some percentage of packets from the result, or is there a more complex model underpinning the exercise? Modeling TCP data rates and latency in similarly losssy scenarios seems much more difficult since dropped packets have cascading effects. Has anyone on this list considered this class of comparison? Any suggestions for modeling software that could aid my search? Thank you
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