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Inside Collection (Course): ELEC 301 Project: Voice Recognition
Results:
Figures showing the efficacy of steps 1-4 (see Methods) are displayed below.
Testing this algorithm in Matlab with the generated input data of ten numbers resulted in a 70% accuracy match, vastly more successful than our attempt at linear prediction coding. However, while 70% is admittedly a decent result in the speech recognition field, one ought to remember that the system faces several important limitations (that were common to the LPC as well).
First, the system is trained by a limited sampling. While it is expected to hold to similar accuracy when tested against other male voices, it will be highly inaccurate when testing female voices. Second, segmentation has shown to work perfectly well with calm, enunciated speech, and recognition to a large degree. The same could not be said of more casual speech where numbers might be slurred or stuttered, or non-numerical noises inserted (i.e. “um” or “ah”). Similarly, some speakers might prefer to speak in terms of multiple digits - “seventy” instead of “seven-oh”, for instance. A more robust system would take these issues into account.
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This work is licensed by Kevin Chu under a Creative Commons Attribution License (CC-BY 3.0), and is an Open Educational Resource.
Last edited by Kevin Chu on Dec 19, 2011 9:07 pm -0600.
