Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
This addresses the challenge of extracting semantic information from speech for natural language processing, though it is incremental as it adapts existing text-based methods to audio.
The authors tackled the problem of learning word embeddings directly from speech, proposing Speech2Vec, which outperformed Word2Vec on 13 word similarity benchmarks.
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. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions.