Learning Joint Acoustic-Phonetic Word Embeddings

Mohamed El-Geish

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arXiv · 1908.00493

Learning Joint Acoustic-Phonetic Word Embeddings

Mohamed El-Geish

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Abstract

Most speech recognition tasks pertain to mapping words across two modalities: acoustic and orthographic. In this work, we suggest learning encoders that map variable-length, acoustic or phonetic, sequences that represent words into fixed-dimensional vectors in a shared latent space; such that the distance between two word vectors represents how closely the two words sound. Instead of directly learning the distances between word vectors, we employ weak supervision and model a binary classification task to predict whether two inputs, one of each modality, represent the same word given a distance threshold. We explore various deep-learning models, bimodal contrastive losses, and techniques for mining hard negative examples such as the semi-supervised technique of self-labeling. Our best model achieves an $F_1$ score of 0.95 for the binary classification task.

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Mohamed El-Geish. 2019-08-01. Learning Joint Acoustic-Phonetic Word Embeddings. https://arxiv.org/abs/1908.00493

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