Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks (1904.03416v1)

Abstract: Learning good representations without supervision is still an open issue in machine learning, and is particularly challenging for speech signals, which are often characterized by long sequences with a complex hierarchical structure. Some recent works, however, have shown that it is possible to derive useful speech representations by employing a self-supervised encoder-discriminator approach. This paper proposes an improved self-supervised method, where a single neural encoder is followed by multiple workers that jointly solve different self-supervised tasks. The needed consensus across different tasks naturally imposes meaningful constraints to the encoder, contributing to discover general representations and to minimize the risk of learning superficial ones. Experiments show that the proposed approach can learn transferable, robust, and problem-agnostic features that carry on relevant information from the speech signal, such as speaker identity, phonemes, and even higher-level features such as emotional cues. In addition, a number of design choices make the encoder easily exportable, facilitating its direct usage or adaptation to different problems.

• The paper presents a multi-task self-supervised approach using PASE to extract robust and transferable speech representations.
• It demonstrates superior performance over traditional features with 99.3% speaker identification and 85.3% phoneme recognition accuracies.
• The framework reduces reliance on large labeled datasets and maintains resilience in noisy, real-world speech recognition applications.

Essay on "Learning Problem-Agnostic Speech Representations from Multiple Self-supervised Tasks"

The paper "Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks" presents an innovative approach for deriving speech representations through self-supervised learning. This research introduces a method where a neural encoder, called the Problem-agnostic Speech Encoder (PASE), is employed to learn generalized speech features by concurrently addressing multiple self-supervised tasks. Employing such a framework, the PASE model is designed to extract robust and transferable speech representations that hold fundamental information relevant to various speech characteristics and tasks.

Self-supervised Learning Framework

Central to the proposed method is the construction of an encoder that processes raw speech waveforms and synthesizes them into latent representations. This encoder is unique in that it cooperates with a cohort of workers, each designed to perform distinct self-supervised learning tasks. Such a multi-task learning environment promotes diversity in the learned features and enforces a holistic understanding of the speech signal's intrinsic structure. The experimental configuration details a collection of regression and discrimination tasks that range from waveform autoencoders to global information maximization. These tasks ensure that the learned embeddings encapsulate a broad array of speech attributes, including speaker identity and prosodic elements.

Empirical Validation and Findings

The experimental results illustrate the efficacy of the PASE model across various speech recognition tasks, including speaker identification, emotion classification, and automatic speech recognition. Notably, the paper reports superior performance of the PASE’s features in comparison to traditional handcrafted features such as MFCCs and FBANKs. The experiments further confirm that the learned representations exhibit a remarkable degree of transferability and robustness against acoustic distortion and variance—critical characteristics for practical deployment in real-world applications.

Quantitatively, the PASE features, when fine-tuned within a supervised framework, achieved an impressive speaker identification accuracy of 99.3% on a challenging subset of the VCTK dataset. Additionally, the model attained a phoneme recognition rate of 85.3% on the TIMIT dataset, indicating a potent capability to generalize phonetic and speaker-level information. In noise and reverberation-prone environments, as evaluated on the DIRHA dataset, the PASE maintained a competitive advantage over traditional features, demonstrating its potential for robust speech parsing.

Theoretical and Practical Implications

Theoretical advancement in speech processing is a notable implication of this research, especially with regard to how self-supervised tasks can be orchestrated to derive meaningful and adaptable speech features. Practically, this approach reduces the dependency on large labeled datasets, which are often expensive and laborious to produce, thus opening pathways for more accessible large-scale speech processing. Furthermore, the PASE framework's flexibility allows for potential extensions, including potentially semi-supervised augmentations and suitability for diverse speech-related applications.

Future Directions in AI

This research indicates promising trajectories in AI involving the more widespread adoption and adaptation of self-supervised learning for multi-modal data. The architecture can inspire extensions into visual or audiovisual domains, providing a scalable solution that aligns with minimal supervision learning paradigms. There is also the scope for integrating additional self-supervised tasks within the training framework of PASE, possibly extending its utility to more nuanced or domain-specific speech processing tasks.

In conclusion, by employing a collection of self-supervised tasks, this paper contributes a significant advancement towards the goal of generating versatile and robust speech representations that are agnostic of particular problem scopes. This opens new doors in both theoretical exploration and practical application in the AI-driven analysis of auditory signals.