Project Waifu: Speaker Verification

Project Waifu

Project Waifu is a long-term machine learning/deep learning project I will be working on. I will not reveal too much about it, but here’s the first part of the pipeline: speaker verification.

Text-Independent Speaker Verification

Speaker verification is the process of recognizing the identity of the speaker which in this case, is either 1 (is who we want to identify) or 0 (not the person). A lot of algorithms online uses GMMs and/or creates profiles for speakers. For this project, a MLP (multi-layer perception – regular feed-forward neural network) is used and because of the way it is structured, the algorithm performs pretty well.

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Predicting Website Credibility Using a DNN

Over the last few weeks I’ve been working on a deep neural net to predict website credibility (i.e. how “reliable” it is). The features consist of basic website features such as its domain and a bag-of-words model.

Website Credibility

Website credibility is determined by a lot of things and a lot of the time there isn’t a right or wrong answer. Wikipedia, for example, is a┬ánotorious source because it can be edited by anyone. Nonetheless, Wikipedia does contain a lot of correct and is still considered unreliable.

Although there is no exact answer, we can often predict the credibility through many features such as the author, the “purpose” of the text, and even the date. (More can be found here)

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