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