Streaming ASR Self-Alignment for Lower Prediction Delay
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Solution Overview
Problem
Existing streaming automatic speech recognition (ASR) models suffer from high prediction delays due to reliance on future audio context, and conventional methods to reduce delay either require high-precision external alignment models or blindly choose suboptimal paths, complicating training and degrading performance.
Innovation Solution
Implement self-alignment in streaming speech recognition models, leveraging reference forced-alignments to choose an optimal low-latency path without external alignment models, by constraining the decoding graph to one frame left of Viterbi alignments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a streaming ASR model is used to enable real-time speech processing, then the system can process speech continuously without buffering, but the model delay increases due to the need for self-alignment of acoustic features with text transcripts
Solution Approach 1:
The system performs preliminary actions by pre-processing audio data through feature extraction and creating acoustic representations before the main ASR processing. The self-alignment mechanism pre-adjusts the acoustic features to match expected temporal patterns, preparing the data in advance to reduce processing delays during real-time operation.
Solution Approach 2:
The system implements dynamic adjustments through the self-alignment mechanism that continuously adapts acoustic feature timing to match text transcript synchronization requirements. The model dynamically rescales and adjusts temporal characteristics of acoustic features during processing, allowing flexible adaptation to varying speech rates and maintaining real-time performance.
2Speed
If the ASR model processes speech in real-time with continuous processing, then responsiveness is improved, but synchronization between acoustic features and text transcripts becomes difficult to maintain
Solution Approach 1:
The self-alignment mechanism implements feedback by comparing the temporal characteristics of acoustic features with the expected timing from text transcripts. The system continuously monitors synchronization status and adjusts the acoustic feature timing accordingly, creating a closed-loop control system that maintains precise alignment despite real-time processing variations.
Solution Approach 2:
The system changes temporal parameters of acoustic features through rescaling and time-stretching operations. By dynamically adjusting the time axis of acoustic representations while preserving their spectral characteristics, the model maintains synchronization accuracy without sacrificing processing speed, allowing flexible adaptation to different speech rates and durations.
3Productivity
If the model uses self-alignment to reduce delay, then processing efficiency is improved, but the model complexity increases due to additional alignment mechanisms
Solution Approach 1:
The self-alignment mechanism operates autonomously within the ASR model, using the model's own internal representations to perform alignment without requiring external control systems or additional hardware. The acoustic features self-adjust their temporal characteristics based on intrinsic relationships with the text transcript, reducing the need for complex external synchronization infrastructure.
Solution Approach 2:
The self-alignment mechanism serves multiple functions simultaneously: it performs temporal synchronization, adjusts feature timing, and maintains synchronization across different speech conditions. By integrating these functions into a single unified mechanism within the transformer model, the system avoids the complexity of separate dedicated systems for each function while maintaining processing efficiency.
Data Source
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AI summary
A streaming speech recognition model (200) includes an audio encoder (210) configured to receive a sequence of acoustic frames (110) and generate a higher order feature representation (202) for a corresponding acoustic frame in the sequence of acoustic frames. The streaming speech recognition model also includes a label encoder (220) configured to receive a sequence of non-blank symbols output (242) by a final softmax layer (240) and generate a dense representation (222). The streaming speech recognition model also includes a joint network (230) configured to receive the higher order feature representation generated by the audio encoder and the dense representation generated by the label encoder and generate a probability distribution (232) over possible speech recognition hypotheses. Here, the streaming speech recognition model is trained using self-alignment to reduce prediction delay by encouraging an alignment path that is one frame left from a reference forced-alignment frame.