Unsupervised Mismatch Localization in Cross-Modal Sequential Data

• URL: http://arxiv.org/abs/2205.02670v1
• Date: Thu, 5 May 2022 14:23:27 GMT
• Title: Unsupervised Mismatch Localization in Cross-Modal Sequential Data
• Authors: Wei Wei, Huang Hengguan, Gu Xiangming, Wang Hao, Wang Ye
• Abstract summary: We develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal data. We propose a hierarchical Bayesian deep learning model, named mismatch localization variational autoencoder (ML-VAE), that decomposes the generative process of the speech into hierarchically structured latent variables. Our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations.
• Score: 5.932046800902776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment algorithms assume the content involved in the two modalities is perfectly matched and thus leading to difficulty in locating such mismatch between speech and text. In this work, we develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal sequential data, especially for speech-text sequences. More specifically, we propose a hierarchical Bayesian deep learning model, named mismatch localization variational autoencoder (ML-VAE), that decomposes the generative process of the speech into hierarchically structured latent variables, indicating the relationship between the two modalities. Training such a model is very challenging due to the discrete latent variables with complex dependencies involved. We propose a novel and effective training procedure which estimates the hard assignments of the discrete latent variables over a specifically designed lattice and updates the parameters of neural networks alternatively. Our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations for model training.

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