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The following are some example outputs from the neural network from various test speakers. The output displaysrelative strengths of different types of accents prevalent in a particular subject. All test inputs were not used in the trainingmatrix. Overall, approximately 20 tests were conducted with about an 80% success rate. Those that failed tended to with good reason(either inadequate recording quality, or speakers who did not provide accurate information about what their accent is comprisedof – a common issue with subjects who have lived in multiple places).
The charts below show accents in the following order: Northern US, Texan US, Russian, Farsi, and Mandarin
Here our network has successfully picked out the accent of our subject. Secondarily, the network picked up on a slight Texan accent, possibly showing the influence of location on the subject (The sample was recorded in Texas).
Again our network has successfully picked out the accent of our subject. Once again, this sample was recorded in Texas, which could account for the secondary influence of a Texan accent in the subject.
Once again, the network successfully picks up on the subjects primary accent as well as influence of a Texan accent (this sample was also recorded in Texas).
A successful test showing little or no influence from other accents in the network.
Results from a subject who has lived all over -- mainly in Texas, who's accent appears to sound more Northern (which seems relatively true if one listens to the source recording).
Successful test of a Russian subject with strong influences of a Northern US accent.
Another successful test of a Russian subject with strong influences of a Northern US accent.
Successful region-based test of a Cantonese subject who has been living in the US.
An interesting example of throwing an accent at the network that doesn't fit into any of the categories.
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