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I just came up with an idea that I want to develop into an application to distinguish/auto detect voices from different people.
Sample use case: After training with Obama and Romney's data, the application would be able to detect whenever either one speak again (not necessary the same content from the training data)
I am wondering if there are any existing research on this. (I don't know how to search for this. I tried a couple keywords and got no significant results.)
If not, what is a good way to start? How to choose features, data representation, models, etc.
Thanks!
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Voice recognition is a deep learning technique used to identify, distinguish, and authenticate a particular person's voice. It evaluates an individual's unique voice biometrics, including frequency and flow of pitch, and natural accent.
Essentially, a person's voice is used as a unique identifier. A sample of their speech is recorded. Then, that person's speech patterns are compared against a database to see if there is a match between the person's voice and their voice sample.
Imprecision and false interpretations. Speech recognition software isn't always able to interpret spoken words correctly. This is due to computers not being on par with humans in understanding the contextual relation of words and sentences, causing misinterpretations of what the speaker meant to say or achieve.
Among the best voice recognition apps, there are also those with the translation feature. One worth recommending is Jibbigo. It's a two-way translation app available on iOS and Android.
I found Speaker recognition on Wikipedia which in turn linked to An overview of text-independent speaker recognition: From features to supervectors (Kinnunen, Li, 2010).
From the abstract of the paper:
This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods.
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