Spoken Word2Vec: A Perspective And Some Techniques
Mohammad Amaan Sayeed Hanan Aldarmaki
Text word embeddings that encode distributional semantic features 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 similar 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 Word2Vec algorithms fail to encode distributional semantics
when the input units are acoustically correlated. In addition, previous works
relied on the simplifying assumptions of perfect word segmentation and
clustering by word type. Given these conditions, a trivial solution identical
to text-based embeddings has been overlooked. We follow this simpler path using
automatic word type clustering and examine the effects on the resulting
embeddings, highlighting the true challenges in this task.