Spoken Word2Vec: Learning Skipgram Embeddings from Speech
This work addresses the challenge of extracting semantic meaning from spoken words for applications in speech processing and natural language understanding, representing an incremental improvement over previous methods.
The paper tackled the problem of learning semantic embeddings from speech, showing that shallow skipgram-like algorithms fail to encode distributional semantics due to acoustic correlations, and demonstrated that a deep end-to-end variant yields positive results for semantic relatedness in the embedding space.
Text word embeddings that encode distributional semantics work by modeling contextual similarities of frequently occurring words. Acoustic word embeddings, on the other hand, typically encode low-level phonetic similarities. Semantic embeddings for spoken words have been previously explored using analogous algorithms to Word2Vec, but the resulting vectors still mainly encoded phonetic rather than semantic features. In this paper, we examine the assumptions and architectures used in previous works and show experimentally how shallow skipgram-like algorithms fail to encode distributional semantics when the input units are acoustically correlated. We illustrate the potential of an alternative deep end-to-end variant of the model and examine the effects on the resulting embeddings, showing positive results of semantic relatedness in the embedding space.