
Show HN: Demo: most accurate speech recognition - soheil
http://app.loverino.com/#try-the-demo
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PaulHoule
Pricing seems high and also irrational.

I might know that I want to process 10 hours of stuff, but to know I am going
to to do N hours of stuff a month that just seems an excuse for recurring
billing.

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soheil
We replaced it with a free trial.

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fundamental
"We think we have created one of the best speech recognition engines!"

Then why don't you have any numerical evidence for that claim? Speech
processing/recognition isn't a new field by any means, so you should be able
quantify performance for your particular domain.

I don't know if any such numerical results would drive conversions for you,
but it should help you get something more definitive than "we think".

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PaulHoule
Speech recognition is an area where objective evaluation has been as much part
of the problem as the solution.

If the paradigm is "get the highest recognition rate over sample X" you can
have super-human accuracy but the product still sucks because you are saying
"Xbox Play Titanfall" over and over again and it never figures it out because
it doesn't engage you in a dialogue to understand what you are saying.

We've seen the same problem in full text search. The TREC evaluations have,
over the long term, taught us how to make better search engines, but if you
looked at any one year of TREC you would get depressed about the prospect of
making a better search engine because you'll see all the things people tried
that failed.

When Google came out the TREC community was overturned because it was clear
you could make a much "better" search engine, but measured the way TREC
measured things at the time, Google would not have appeared better, because
TREC rewards you for being accurate at position 1000 and Google cares mostly
about position 1. Customers perceive the latter, but they will never get to
1000 unless they are researching a patent application or something.

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fundamental
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.

