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Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data (1709.07902v1)

Published 22 Sep 2017 in cs.LG, cs.CL, cs.SD, eess.AS, and stat.ML

Abstract: We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of information in sequential data by formulating it explicitly within a factorized hierarchical graphical model that imposes sequence-dependent priors and sequence-independent priors to different sets of latent variables. The model is evaluated on two speech corpora to demonstrate, qualitatively, its ability to transform speakers or linguistic content by manipulating different sets of latent variables; and quantitatively, its ability to outperform an i-vector baseline for speaker verification and reduce the word error rate by as much as 35% in mismatched train/test scenarios for automatic speech recognition tasks.

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Authors (3)
  1. Wei-Ning Hsu (76 papers)
  2. Yu Zhang (1400 papers)
  3. James Glass (173 papers)
Citations (345)

Summary

  • The paper introduces a Factorized Hierarchical VAE that disentangles sequence- and segment-level features in speech data.
  • The model employs a sequence-to-sequence LSTM architecture with multi-scale priors to enhance interpretability and scalability.
  • Empirical results demonstrate a 2.38% equal error rate in speaker verification and up to 35% improvement in ASR performance under mismatched conditions.

Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data: A Critical Review

The paper presents a novel approach to unsupervised learning through the development of a Factorized Hierarchical Variational Autoencoder (FHVAE), specifically designed to learn disentangled and interpretable representations from sequential data. The model distinguishes itself by leveraging the inherent multi-scale information present in sequential data, applying a structured factorized hierarchical graphical model. This model introduces sequence-dependent and sequence-independent priors across different latent variables, facilitating the disentangling of latent features without supervision.

Technical Approach

The FHVAE model employs a generative process conditioned on latent variables that capture sequence-level and segment-level attributes separately by imposing different constraints. It capitalizes on a sequence-to-sequence architecture, using Long Short-Term Memory (LSTM) neural networks to effectively capture temporal dependencies within the data. The inference process is adjusted to operate at the segment level, enhancing computational scalability for longer sequences. This design allows the model to infer meaningful representations that adhere to the multi-scale nature of the input data domains, typical in speech and potentially extensible to video and text.

Evaluation and Results

The paper provides robust empirical evaluation using two speech datasets: TIMIT and Aurora-4, to substantiate the proposed model. Quantitative results highlight the model's capacity to outperform traditional i-vector baselines in unsupervised and supervised settings for speaker verification tasks. Notably, a 2.38% equal error rate was achieved, showing a significant reduction compared to the baseline. In automatic speech recognition (ASR) tasks, FHVAE substantially improved word error rates, notably reducing errors by up to 35% under mismatched conditions between training and testing phases. This capability suggests its potential for high-impact applications in developing noise-robust and domain-invariant ASR systems.

Analysis and Implications

The core innovation of FHVAE lies in its ability to independently model sequence-level and segment-level features effectively, affording a disentangled latent space that lends interpretability—an asset for high-stake applications. By separating these features, the model facilitates tasks like speaker identity transformation or denoising in speech data without the necessity for labeled datasets. Thus, the model aligns with pressing needs in deep unsupervised representation learning, offering promise for scalability across various applications requiring sequence data comprehension.

Future Directions

Potential extensions to this work could include the application of FHVAEs to other domains with hierarchical data structures, such as video and text, exploring further levels of hierarchy beyond binary segmentation of sequential attributes. The integration of adversarial training or the combination with other generative models could enhance the interpretation and disentanglement capacity. Additionally, the experimentation could be extended by employing more complex datasets from diverse domains to better illustrate FHVAE's versatility in capturing intricate relationships within sequential data.

This paper represents a notable advancement in the field of unsupervised representation learning, presenting a novel method with tangible results in speech processing tasks, and laying a groundwork for future innovations in the modeling of sequential human-centric data.