Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models (1904.04100v3)

Abstract: Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a relatively big unlabeled data set for such languages is more achievable. This is why some initial effort have been reported on completely unsupervised speech recognition learned from unlabeled data only, although with relatively high error rates. In this paper, we develop a Generative Adversarial Network (GAN) to achieve this purpose, in which a Generator and a Discriminator learn from each other iteratively to improve the performance. We further use a set of Hidden Markov Models (HMMs) iteratively refined from the machine generated labels to work in harmony with the GAN. The initial experiments on TIMIT data set achieve an phone error rate of 33.1%, which is 8.5% lower than the previous state-of-the-art.

• The paper introduces a novel unsupervised ASR system that integrates GANs with HMMs to iteratively refine phoneme segmentation and reduce error rates.
• The iterative GAN-HMM framework achieves an 8.5% improvement in phone error rates on the TIMIT dataset compared to previous unsupervised methods.
• The method scales efficiently with large datasets, paving the way for low-resource language ASR without costly manual data annotation.

Unsupervised Speech Recognition via GAN and HMM Integration

This paper by Chen et al. presents an innovative approach to unsupervised speech recognition that leverages the prowess of Generative Adversarial Networks (GANs) in conjunction with Hidden Markov Models (HMMs). The aim is to address the prevalent issue of Automatic Speech Recognition (ASR) in low-resourced languages, where labeled data is scarce, but unlabeled datasets can be more easily acquired. This work focuses on building an ASR system that is entirely trained on unlabeled data, eliminating the need for expensive and labor-intensive annotation processes.

Key Contributions

1. Integrating GANs with HMMs: The paper proposes a harmonization of GANs with HMMs to iteratively refine and improve the phoneme recognition performance. This process involves training a GAN to map acoustic features to phoneme distributions, followed by an HMM-based forced alignment to refine phoneme segmentations, which are then used to retrain the GAN.
2. Iterative Refinement Framework: The iterative training process, wherein GANs and HMMs learn from each other's outputs, facilitates progressively improved phoneme error rates (PER). This iterative refinement is crucial in adapting to the segmental structure of speech data in the absence of labeled information.
3. Empirical Results: The experimental results on the TIMIT dataset show a remarkable reduction in phone error rate to 33.1%, which is 8.5% lower than the previous state-of-the-art in unsupervised settings. The methodological enhancements include incorporating a data augmentation strategy and minimizing intra-segment variance to enhance the GAN's learning capacity.
4. Comparative Analysis: The paper provides a detailed comparison of the proposed system's performance against several baselines, including supervised approaches. Notably, the PER achieved by the proposed system closely matches that of supervised systems when limited labeled data is used, demonstrating its efficacy.
5. Scalability and Flexibility: The proposed method is designed to scale efficiently with large datasets, overcoming the constraints posed by prior methods that required large batch sizes for satisfactory performance.

Implications and Future Directions

This research advances the field of unsupervised ASR by establishing a feasible framework for training ASR systems without labeled data. Practically, it opens pathways for deploying ASR systems across numerous low-resourced languages without the bottleneck of manual labeling.

Theoretically, the integration of GANs with HMMs offers a novel perspective on handling sequential data in unsupervised machine learning. Future work could explore the application of this framework to other sequence mapping tasks and further refine the GAN components, perhaps by employing more sophisticated variants like Wasserstein GANs to improve stability and performance.

Additionally, expanding the framework to incorporate transfer learning or domain adaptation could potentially enhance its robustness across diverse and challenging acoustic environments. Another avenue for exploration could involve integrating advanced neural architectures like Transformers within this unsupervised framework for more nuanced feature extraction.

In summary, Chen et al. contribute a significant step forward in unsupervised ASR, providing a compelling solution to a long-standing problem in speech recognition research. The proposed GAN and HMM harmonization framework presents a promising basis for future developments in unsupervised learning paradigms.