JEPA as a Neural Tokenizer: Learning Robust Speech Representations with Density Adaptive Attention
Abstract
We introduce a two-stage self-supervised framework that combines the Joint-Embedding Predictive Architecture (JEPA) with a Density Adaptive Attention Mechanism (DAAM) for learning robust speech representations. Stage~1 uses JEPA with DAAM to learn semantic audio features via masked prediction in latent space, fully decoupled from waveform reconstruction. Stage~2 leverages these representations for efficient tokenization using Finite Scalar Quantization (FSQ) and a mixed-radix packing scheme, followed by high-fidelity waveform reconstruction with a HiFi-GAN decoder. By integrating Gaussian mixture-based density-adaptive gating into the JEPA encoder, the model performs adaptive temporal feature selection and discovers hierarchical speech structure at a low frame rate of 2.5~Hz. The resulting tokens (47.5 tokens/sec) provide a reversible, highly compressed, and language-model-friendly representation that is competitive with, and often more efficient than, existing neural audio codecs.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel two-stage self-supervised framework that integrates JEPA with DAAM for efficient speech representation learning, showcasing significant advancements in methodology and practical applications. The technical contributions are well-articulated, and the results demonstrate the potential for impactful applications in the field of audio processing.
Methodology
The proposed methodology introduces a two-stage self-supervised learning framework that effectively decouples representation learning from reconstruction, which is a significant advancement in the field of speech representation. By employing the Joint-Embedding Predictive Architecture (JEPA) in conjunction with a Density Adaptive Attention Mechanism (DAAM), the authors demonstrate a novel approach to learning semantic audio features through masked prediction. The integration of Gaussian mixture-based gating enhances the model's ability to adaptively select informative features, which is a notable contribution to the efficiency and effectiveness of speech representation learning.
Results & evaluation
The experimental setup is robust, utilizing a large-scale dataset (LibriLight) for training, which is crucial for self-supervised learning tasks. The paper outlines a clear evaluation strategy, comparing the proposed method against established baselines, including a JEPA baseline without DAAM and WavLM-Large. The results indicate that the proposed approach not only achieves competitive performance but also excels in efficiency, providing a strong case for its practical applicability in real-world scenarios.
Reproducibility
The authors provide a comprehensive description of the implementation details, including hyperparameters, training procedures, and the architecture of the model. The availability of the complete implementation on GitHub enhances reproducibility, allowing other researchers to validate and build upon their work. However, the paper could benefit from additional details on the training environment and computational resources used.
Limitations
The paper acknowledges several limitations, including a fixed masking strategy that may not adapt optimally to varying speech rates and a focus on English speech, which limits generalizability. Furthermore, the authors note that the scale of data used for pretraining is relatively modest compared to other self-supervised systems, which may affect the robustness of their findings.
Broader impact
The implications of this research are significant, particularly in the context of speech processing applications such as automatic speech recognition, text-to-speech systems, and voice conversion. The ability to learn efficient and robust speech representations has the potential to enhance the performance of various downstream tasks and could lead to advancements in human-computer interaction technologies. The paper presents a novel two-stage self-supervised framework that integrates JEPA with DAAM for efficient speech representation learning, showcasing significant advancements in methodology and practical applications. The technical contributions are well-articulated, and the results demonstrate the potential for impactful applications in the field of audio processing.
Author of this paper? Send a correction.