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I'm currently working on a project that involves in Speech and recognising Phonemes (in kind of real-time).

The current algorithm works by always listening for input from the Mic, and input that is above a specific threshold then this is recorded to a .wav file and sent to the server for processing. E.g.

listening...
listening...
listening.. (max > threshold)
recording..
recording..
etc..

What I want to do is therefore have it recording when a specific name has been said. Someone basically speaks to it.. I.e. (let's assume it's called iris) so the algorithm would therefore work like the following:

listening...
listening..
listening.. (max > threshold)
  // determine if the input is "iris"
  // if true: 
     begin recording the sentence 
  // else:
listening..

I want to do this in the time-domain, then, process the sentences in the frequency domain. I've read an article, and, a paper that describes this process using Total number of Zero-crossings (using a specific threshold) my questions is whether or not this would be a good measure? I only need to identify one word, that is "Iris" and then everything else can be done using algorithms such as: MFCC, HMM's etc..

Any ideas? :)

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  • $\begingroup$ Does this have to be speaker-independent (i.e. work for lots of different people) ? Can you "train" the system? Or does it have to work straight away without any other user interaction? $\endgroup$ – Peter K. Oct 7 '13 at 11:56
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    $\begingroup$ @PeterK. Basically, it has to work with multiple people. But, I read the following: cs.dartmouth.edu/~dwagn/aiproj/speech.html and this guy uses Zero-crossings. But I think I have an alternative solution $\endgroup$ – Phorce Oct 7 '13 at 12:34
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I highly doubt that something as complex as recognizing a word can be done directly in the time domain using features as basic as zero-crossing rate.

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Zero crossing rate is not a good approach indeed, you can use any keyword spotting software to spot for the word, keyword spotting is based on HMM too and statistical hypothesis testing.

You can try keyword spotting with CMUSphinx

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