Yu-An Chung

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Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed. Many such applications also demand varying costs for different types of mis-classification errors, but it is not clear whether or how such cost information can be(More)
Representing audio segments expressed with variable-length acoustic feature sequences as fixed-length feature vectors is usually needed in many speech applications, including speaker identification, audio emotion classification and spoken term detection (STD). In this paper, we apply and extend sequence-to-sequence learning framework to learn(More)
The vector representations of fixed dimensionality for words (in text) offered by Word2Vec have been shown to be very useful in many application scenarios, in particular due to the semantic information they carry. This paper proposes a parallel version, the Audio Word2Vec. It offers the vector representations of fixed dimensionality for variable-length(More)
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