Confusion2Vec: towards enriching vector space word representations with representational ambiguities

Area of Science:

• Natural Language Processing (NLP)
• Human-Computer Interaction
• Speech Recognition

Background:

• Word vector representations are fundamental to NLP and human-computer interaction.
• Existing methods often overlook representational ambiguity, particularly acoustic similarity cues.
• Humans leverage both acoustic and contextual information to resolve ambiguous speech.

Purpose of the Study:

• To propose Confusion2Vec, a novel word vector representation incorporating acoustic and contextual ambiguity.
• To apply Confusion2Vec to automatic speech recognition (ASR) by modeling acoustic word confusions.
• To evaluate the model's ability to enrich word vector spaces with task-relevant ambiguity information.

Main Methods:

• Developed Confusion2Vec, a model trained on unsupervised data from ASR confusion networks.
• Incorporated acoustic perceptual similarity and contextual cues into word representations.
• Utilized principal component analysis for intuitive exploration of the 2D Confusion2Vec space.

Main Results:

• Confusion2Vec efficiently models word confusions without compromising semantic-syntactic relations.
• The representation effectively enriches word vector spaces with ambiguity information.
• Evaluations demonstrated robust encoding of acoustic similarities alongside semantic and syntactic relationships.

Conclusions:

• Confusion2Vec offers a novel approach to word vector representation by encoding ambiguity.
• The model shows significant potential for improving ASR tasks, particularly error correction.
• This representation effectively utilizes uncertainty information from lattice structures.