To an extent I have to agree. Trying to figure out how to measure success is painful and each objective is going to have some fault. The key here is "for your p

To an extent I have to agree. Trying to figure out how to measure success is painful and each objective is going to have some fault. The key here is "for your particular domain". The particular target of "voice journals" would indicate single individual, relatively low background noise, microphones which could be found in common smartphones, etc. Per general recognition, success is going to be biased towards successfully identifying words which would actually be searched for. Given the high price having some sort of evaluation beyond do it yourself would seem reasonable to me. I find it somewhat hard to take something seriously which describes itself with: - "Finally a high quality X" (relative to what?) - "We think" (that sure doesn't make it sound like you've done your research) - "trained on countless hours of audio" (Massive understatement given typical speech recog. projects) - "best possible accuracy" (ok, where/how have you measured that) I haven't gone into the full text search field, but I've seen plenty of areas where measuring success is a convoluted mess, which doesn't necessarily lead to better numbers in real world scenarios. Computer vision in particular seems to have plenty of these local optima scattered around.