  Word vector representations enable machines to encode human language for
spoken language understanding and processing. Confusion2vec, motivated from
human speech production and perception, is a word vector representation which
encodes ambiguities present in human spoken language in addition to semantics
and syntactic information. Confusion2vec provides a robust spoken language
representation by considering inherent human language ambiguities. In this
paper, we propose a novel word vector space estimation by unsupervised learning
on lattices output by an automatic speech recognition (ASR) system. We encode
each word in confusion2vec vector space by its constituent subword character
n-grams. We show the subword encoding helps better represent the acoustic
perceptual ambiguities in human spoken language via information modeled on
lattice structured ASR output. The usefulness of the proposed Confusion2vec
representation is evaluated using semantic, syntactic and acoustic analogy and
word similarity tasks. We also show the benefits of subword modeling for
acoustic ambiguity representation on the task of spoken language intent
detection. The results significantly outperform existing word vector
representations when evaluated on erroneous ASR outputs. We demonstrate that
Confusion2vec subword modeling eliminates the need for retraining/adapting the
natural language understanding models on ASR transcripts.
