Learning Joint Articulatory-Acoustic Representations with Normalizing Flows

• URL: http://arxiv.org/abs/2005.09463v2
• Date: Thu, 1 Oct 2020 03:54:41 GMT
• Title: Learning Joint Articulatory-Acoustic Representations with Normalizing Flows
• Authors: Pramit Saha, Sidney Fels
• Abstract summary: We find a joint latent representation between the articulatory and acoustic domain for vowel sounds via invertible neural network models. Our approach achieves both articulatory-to-acoustic as well as acoustic-to-articulatory mapping, thereby demonstrating our success in achieving a joint encoding of both the domains.
• Score: 7.183132975698293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The articulatory geometric configurations of the vocal tract and the acoustic properties of the resultant speech sound are considered to have a strong causal relationship. This paper aims at finding a joint latent representation between the articulatory and acoustic domain for vowel sounds via invertible neural network models, while simultaneously preserving the respective domain-specific features. Our model utilizes a convolutional autoencoder architecture and normalizing flow-based models to allow both forward and inverse mappings in a semi-supervised manner, between the mid-sagittal vocal tract geometry of a two degrees-of-freedom articulatory synthesizer with 1D acoustic wave model and the Mel-spectrogram representation of the synthesized speech sounds. Our approach achieves satisfactory performance in achieving both articulatory-to-acoustic as well as acoustic-to-articulatory mapping, thereby demonstrating our success in achieving a joint encoding of both the domains.

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