Self-Supervised Speech Representation Learning: A Review (2205.10643v3)

Abstract: Although supervised deep learning has revolutionized speech and audio processing, it has necessitated the building of specialist models for individual tasks and application scenarios. It is likewise difficult to apply this to dialects and languages for which only limited labeled data is available. Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Such methods have shown success in natural language processing and computer vision domains, achieving new levels of performance while reducing the number of labels required for many downstream scenarios. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. Other approaches rely on multi-modal data for pre-training, mixing text or visual data streams with speech. Although self-supervised speech representation is still a nascent research area, it is closely related to acoustic word embedding and learning with zero lexical resources, both of which have seen active research for many years. This review presents approaches for self-supervised speech representation learning and their connection to other research areas. Since many current methods focus solely on automatic speech recognition as a downstream task, we review recent efforts on benchmarking learned representations to extend the application beyond speech recognition.

Overview of Self-Supervised Speech Representation Learning

The paper "Self-Supervised Speech Representation Learning: A Review" provides a comprehensive survey of the methods and advancements in self-supervised learning (SSL) applied to the domain of speech processing. Supervised deep learning, while transformative for speech and audio processing tasks, requires task-specific models and extensive labeled data. This constraint poses challenges when dealing with languages or dialects with limited labeled resources. SSL has emerged as a promising alternative, allowing for the training of universal models that perform well across diverse tasks and domains with fewer labeled data. The paper details the various approaches in speech representation learning, classifying them into generative, contrastive, and predictive methods. Additionally, it examines the synergy between multi-modal data and SSL, analyzing the historical context and potential future of SSL in speech research.

Key Insights and Methodologies

Generative Approaches: These methods involve reconstructing input data from partial views. Techniques explored include autoencoding models like VAEs and novel masked reconstruction strategies inspired by NLP transformers. Models such as APS and VQ-VAEs utilize latent feature extraction for effective representation learning, aiming to capture generalized speech signal characteristics.

Contrastive Approaches: Contrastive methods focus on distinguishing similar targets from distractors based on anchor representations. Techniques like Contrastive Predictive Coding (CPC) and wav2vec frameworks expand upon this idea, achieving robust performance across speech tasks by leveraging learned latent space relations.

Predictive Approaches: These methods circumvent the contrastive loss framework by using learned targets. This category includes models like HuBERT and data2vec, which emphasize task consistency and utilization of multi-layer architectures to capture linguistic features.

Multi-Modal Data Exploration: The integration of visual and text modalities with speech data enhances performance by offering complementary information. Research continues to unravel the potential of these combined insights for improving domain resilience and capturing semantic nuances.

Evaluation and Benchmarking

The paper underscores the importance of comprehensive benchmarking datasets and evaluation methodologies. SSL models are evaluated across phoneme recognition, speaker identification, sentiment analysis, and more, underscoring the versatility of learned representations. Datasets like LibriSpeech and Common Voice, alongside benchmarks such as SUPERB, represent critical resources for assessing SSL's practical efficacy.

Challenges and Future Directions

Despite SSL's impressive advances, several challenges remain. The computational burden of large-scale unsupervised pre-training and the intricacies of model scalability and robustness pose significant hurdles. Future work must address these issues, possibly exploring multi-modal representations and more adaptive SSL frameworks, thus facilitating broader applicability across diverse linguistic landscapes and edge devices.

Implications and Speculating Future Developments

The potential for SSL in revolutionizing speech processing is immense. The ability to leverage vast quantities of unlabeled data aligns well with scenarios where labeling is impractical or infeasible. As research progresses, further breakthroughs in zero-resource methods could minimize reliance on transcriptions altogether. Moreover, creative integrations with existing pre-trained models from related domains, such as NLP, could further enhance the semantic understanding and utility of speech models. These developments will undoubtedly alter the landscape of AI, with self-supervised models at the forefront of speech technology innovation.