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In this paper, we propose a novel deep neural network architecture,Speech2Vec, for learning fixed-length vector representations of audio segmentsexcised from a speech corpus, where the vectors contain semantic informationpertaining to the underlying spoken words, and are close to other vectors inthe embedding space if their corresponding underlying spoken words aresemantically similar. The proposed model can be viewed as a speech version ofWord2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrowsthe methodology of skipgrams or continuous bag-of-words for training. Learningword embeddings directly from speech enables Speech2Vec to make use of thesemantic information carried by speech that does not exist in plain text. Thelearned word embeddings are evaluated and analyzed on 13 widely used wordsimilarity benchmarks, and outperform word embeddings learned by Word2Vec fromthe transcriptions.

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