Even if the models have been around for a while, the fact that Google can pull billions of words and phrases from its own data sets — and actually choose the most meaningful data set for any given situation — is significant to understanding how it does speech recognition.

A simple use case that these systems still seem to fail is looking up a contact phone number by voice.

Soundex was a simple algorithm for finding matches used by 411 services. It works especially well when doing speech recognition against a limited domain to overcome limitations in determing what word is said in a critical part of a search.

I have had so many “fails” when I try to say “call XXX” and the voice recognition can’t find XXX in my contacts list. Names are an especially hard voice recognition problem because of the variety of pronounciations.

Yet, it is evident to me that these names could easily (and were obviously the only) match using Soundex, or a variation of that. If there are more than one match, just give a list of choices instead of “can’t find”.

Really big data is nice, but context and use case matters. Optimizing the most common use case/context-specific applications with some different learning/context-specific algorithms can help tremendously in improving this stuff too.

A few use cases done really well would make me happier than my current experience.

My two cents.

Apparently this author has nothing better to do than write articles of no value, that take an obvious banality, make a big deal out of it, and then fail in even explaining what he promised to do. I am reminded of ‘Requiem for a Dream’: We got a winner! OoooOOOOOOOOH! Be exited! Be, be exited!