Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

23 Mar 2018Yu-An ChungJames Glass

In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec... (read more)

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