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Streaming Anchor Loss: Augmenting Supervision with Temporal Significance

Utkarsh Oggy Sarawgi, John Berkowitz, Vineet Garg, Arnav Kundu, Minsik Cho, Sai Srujana Buddi, Saurabh Adya, Ahmed Tewfik

TL;DR

The paper introduces Streaming Anchor Loss (SAL), a frame-wise loss that emphasizes frames near task-critical anchors in time-series data to improve training efficiency for streaming models on resource-constrained devices. SAL multiplies the standard frame-wise cross-entropy by a temporal weight that is highest at the anchor and decays with distance, with focal variants SA+FL and SAFL further emphasizing difficult or anchor-proximate frames. Across keyword spotting, multi-modal trigger detection, and speech onset detection, SAL and its variants yield higher accuracy and lower latency than conventional losses, without additional data or model parameters. The approach leverages domain-specific anchor points to guide learning, and holds promise for extending to multi-class tasks and weakly supervised scenarios. Its practical impact lies in enabling faster, more accurate streaming systems for real-time speech and sensor applications on edge hardware.

Abstract

Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.

Streaming Anchor Loss: Augmenting Supervision with Temporal Significance

TL;DR

The paper introduces Streaming Anchor Loss (SAL), a frame-wise loss that emphasizes frames near task-critical anchors in time-series data to improve training efficiency for streaming models on resource-constrained devices. SAL multiplies the standard frame-wise cross-entropy by a temporal weight that is highest at the anchor and decays with distance, with focal variants SA+FL and SAFL further emphasizing difficult or anchor-proximate frames. Across keyword spotting, multi-modal trigger detection, and speech onset detection, SAL and its variants yield higher accuracy and lower latency than conventional losses, without additional data or model parameters. The approach leverages domain-specific anchor points to guide learning, and holds promise for extending to multi-class tasks and weakly supervised scenarios. Its practical impact lies in enabling faster, more accurate streaming systems for real-time speech and sensor applications on edge hardware.

Abstract

Streaming neural network models for fast frame-wise responses to various speech and sensory signals are widely adopted on resource-constrained platforms. Hence, increasing the learning capacity of such streaming models (i.e., by adding more parameters) to improve the predictive power may not be viable for real-world tasks. In this work, we propose a new loss, Streaming Anchor Loss (SAL), to better utilize the given learning capacity by encouraging the model to learn more from essential frames. More specifically, our SAL and its focal variations dynamically modulate the frame-wise cross entropy loss based on the importance of the corresponding frames so that a higher loss penalty is assigned for frames within the temporal proximity of semantically critical events. Therefore, our loss ensures that the model training focuses on predicting the relatively rare but task-relevant frames. Experimental results with standard lightweight convolutional and recurrent streaming networks on three different speech based detection tasks demonstrate that SAL enables the model to learn the overall task more effectively with improved accuracy and latency, without any additional data, model parameters, or architectural changes.
Paper Structure (11 sections, 5 equations, 1 figure, 3 tables)

This paper contains 11 sections, 5 equations, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Methods and Materials
  3. Notation and Setup
  4. Streaming Anchor Loss
  5. Focal variations of Streaming Anchor Loss
  6. Anchors for different streaming tasks
  7. Experiments and Results
  8. Keyword Spotting
  9. Multi-modal Trigger Detection
  10. Speech Onset Detection
  11. Discussion and Future Work

Figures (1)

  • Figure 1: Toy examples for (a) Keyword Spotting (KWS) and (b) Speech Onset Detection (SOD) showing the corresponding task anchors, $A_{KWS}$ and $A_{SOD}$, respectively (Section \ref{['sec:anchor']}), and how the streaming anchor loss (SAL) modulates the frame-wise cross entropy loss (FCEL) with a frame-wise multiplicative factor $w_t$, referred to as loss weights in the plots above, such that $l_{SAL} = w_t * l_{FCEL}$ ($w_t$ in green) and $l_{FCEL} = w_t * l_{FCEL}$ ($w_t$ in red).