- The paper introduces STAR, a method that dynamically segments and compresses input streams using anchor representations for improved streaming transduction.
- It achieves nearly lossless compression at a 12× rate and reduces memory usage by over 30%, demonstrating efficiency in speech-to-text tasks.
- STAR maintains robust performance in noisy conditions, enabling low-latency real-time applications like ASR and simultaneous translation.
Introduction
Sequence transduction, the task of converting one sequence of symbols into another, is a cornerstone of various machine learning applications. Speech recognition (ASR) and machine translation are two key domains where sequence-to-sequence models have excelled. Traditional transduction methods, however, are predominantly devised for settings where the input is fully available before output generation. This mode becomes ineffective when real-time performance with low latency is essential, as is the case in simultaneous translation and streaming ASR tasks. A new methodology, Stream Transduction with Anchor Representations (STAR), cleverly addresses these issues.
Methodology
STAR introduces a transformative concept whereby input streams are dynamically segmented and then compacted into an efficient anchor-based representation. This concept is pivotal in balancing latency, memory footprint, and quality—three attributes critical in streaming scenarios. STAR operates by scoring input features dynamically through a learnable segmenter. When key segments are identified, each piece undergoes compression to form anchor representations that aggregate previous information essential for generating subsequent output. This approach markedly deviates from methods such as Continuous Integrate and Fire (CIF), which continuously integrate input until a threshold is reached and then fire off an average representation of that input chunk.
STAR’s potency is demonstrated through its compelling performance on established speech-to-text tasks, outstripping existing methods. Specifically, in ASR, STAR achieves a nearly lossless compression at a 12× compression rate. The model shows less degradation in performance even when tested on higher compression rates, indicating its robust representation capabilities in various settings. Furthermore, within the stringent confines of memory usage, STAR efficaciously reduces memory consumption substantially, by over 30%, when dealing with longer sequences.
Robustness and Extensions
The resilience of STAR extends beyond just memory efficiency. When faced with noise-augmented input data—simulating real-world acoustic environments—STAR maintains robust performance, showcasing less decline in comparison to other methods and even exceeding the vanilla S2T model without compression under high noise conditions. This resilience evidences an anchor representation capable of encoding substantial information irrespective of suboptimal circumstances. Consequently, STAR holds promise not only for tasks where streaming is essential but also in applications where input signals are imperfect.
STAR’s architecture fosters efficient deployment in real-time applications where both latency and quality are paramount. This stream transduction approach consolidates the versatility of sequence-to-sequence models in streaming scenarios, underscoring the potential of compression methods tailored towards dynamic segmentation. With ongoing research and development, such frameworks may soon extend to non-autoregressive models and other generative AI avenues, unlocking new strides in real-time sequence modeling.
