Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependencies (2011.00406v1)

Abstract: Self-supervised speech representations have been shown to be effective in a variety of speech applications. However, existing representation learning methods generally rely on the autoregressive model and/or observed global dependencies while generating the representation. In this work, we propose Non-Autoregressive Predictive Coding (NPC), a self-supervised method, to learn a speech representation in a non-autoregressive manner by relying only on local dependencies of speech. NPC has a conceptually simple objective and can be implemented easily with the introduced Masked Convolution Blocks. NPC offers a significant speedup for inference since it is parallelizable in time and has a fixed inference time for each time step regardless of the input sequence length. We discuss and verify the effectiveness of NPC by theoretically and empirically comparing it with other methods. We show that the NPC representation is comparable to other methods in speech experiments on phonetic and speaker classification while being more efficient.

• The paper presents Non-Autoregressive Predictive Coding (NPC), a novel approach that leverages local dependencies to learn speech representations efficiently.
• It employs masked convolution blocks with fixed receptive fields to prevent information leakage and avoid global dependency, enhancing model speed.
• Empirical results show NPC achieves competitive phone (27.9% error) and speaker (6.1% error) classification accuracy while significantly reducing inference latency.

Non-Autoregressive Predictive Coding for Speech Representation from Local Dependencies

The paper by Alexander H. Liu, Yu-An Chung, and James Glass from MIT addresses the inefficiencies inherent in current models used for self-supervised speech representation learning. The authors propose Non-Autoregressive Predictive Coding (NPC), a novel method designed to derive speech representations by focusing solely on local dependencies, thereby eliminating the autoregressive nature and the reliance on global dependencies present in existing methods.

Background and Motivation

Self-supervised learning has demonstrated effectiveness in extracting informative speech representations, useful for tasks such as phonetic content extraction and speaker classification. Conventional approaches, including Contrastive Predictive Coding (CPC) and Autoregressive Predictive Coding (APC), typically require the representation to depend on past inputs, leveraging global sequence dependencies. This characteristic affects the time and computational efficiency of the models, particularly for long sequences where autoregressive processing cannot be easily parallelized.

The NPC model addresses these inefficiencies by using masked convolution blocks that restrict representations to depend only on local sequences with a fixed receptive field. This non-autoregressive approach allows for significant computational speed-ups as the model's inference time is independent of sequence length.

Methodology

The proposed NPC model is built using stacked convolution blocks, each coupled with a masked convolution operation. The masking ensures that frames surrounding the target frame are not directly observed, preventing potential information leakage. The architecture restricts the feature generation process to local contexts, and the model is tasked with minimizing the L1 distance between predicted and actual surface features using a vector quantization layer to serve as an information bottleneck.

Numerical Results and Implications

Empirical results show that the representations learned through NPC are comparable to those produced by conventional methods in terms of phone and speaker classification accuracy. However, NPC considerably improves inference efficiency — a critical advantage for applications requiring low-latency responses, such as streaming speech recognition.

The paper presents detailed ablation studies and analyses on mask and receptive field sizes, demonstrating NPC's robustness and efficiency. Notably, the model achieves a phone error rate of 27.9% and a speaker error rate of 6.1% while maintaining efficient processing speeds significantly better than autoregressive models.

Future Developments

The implications of NPC suggest a promising direction for developing efficient self-supervised models within artificial intelligence, particularly for tasks involving large-scale sequence data. The insights into local dependency utilization open avenues for further research into optimizing energy consumption and scalability of AI systems deployed in real-time environments.

Practical and Theoretical Considerations

While NPC excels in computational efficiency, its performance in global tasks, like speaker classification, slightly trails other methods requiring broader sequence context. This indicates a potential area for future refinement in balancing local and global dependency needs within self-supervised frameworks. Moreover, theoretical exploration of dynamic masking strategies could enhance adaptability of NPC models across varying sequence patterns, further enhancing their utility in diverse applications.

In conclusion, the NPC model represents a substantial advancement in speech representation learning, offering efficiency improvements with performance integrity, serving as a basis for future exploration and innovation in utilizing local dependency frameworks in AI.