Is Speech Recognition Finally 'Good Enough'?

jcatcw writes "Speech recognition software is fast, but it still may not be accurate enough. Clerical jobs usually ask for 40 wpm, but speech recognition software can keep up with someone speaking at 160 wpm. In Lamont Wood's demo it did very well at too/two/to and which/witch, but will it still render 'I really admire your analysis' as "I really admire urinalysis'? At 95% accuracy, people aren't jumping on the bandwagon. Wood's typing speed is about 60 wpm with 93% accuracy, so he found that using speech recognition was about twice as fast as typing. Those who type at hunt-and-peck speeds will experience results that are even more dramatic. There's really only one product on the US market: Dragon NaturallySpeaking from Nuance Communications. The free versions from Microsoft aren't up to the task and IBM sold ViaVoice to Nuance, where it's treated as an entry-level product."

Comments

Until we get hard ai along with it no.

[otomo_1001]

I mean really, until I can say to my computer things like:

Find all mp3's that were created by Trent Reznor and pipe them to /dev/audio on the neighbors computer. What use will it be?

I can't program in it can I?

if(i_can_write_code_I_mean_speak_code_to_the_compu ter() == true) then
    i_might_use_it_a_bit();
else
    system("find /music -type f -name \"*trent*reznor*\" | xargs -t cat - | ssh hackeduser@neighborcomputer \"cat - > /dev/audio\"");
endif

But that is just me.

Re:Hmmm....

[Helios1182]

There is a lot of work on word prediction and language modeling in natural language programming and computational linguistics research. 95% accuracy is considered very good though. There are ways to help, but some of the most effective ways require a constriction of the language recognized. n-gram based language models provide a good statistical framework, but are very data hungry. You need lots and lots of relevant (this is the hard part) text. The model needs to be based on the language the user uses in order to be effective.