Two-Pass ASR Segmentation for Low-Latency Speech Finalization
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Solution Overview
Problem
Integrating segmentation with cascaded encoding in automatic speech recognition (ASR) models introduces user-perceived latency and deletion errors, particularly in applications requiring quick finalization of long-form utterances.
Innovation Solution
A unified end-to-end segmenter and two-pass cascaded encoder ASR model that predicts an end-of-speech token in real-time during the first pass, allowing the second pass to finalize in parallel with additional right-context frames, thereby reducing latency and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation is integrated with cascaded encoding in ASR models, then speech recognition accuracy is improved, but user-perceived latency increases
Solution Approach 1:
The utterance is divided into segments using an end-to-end segmenter that predicts segmentation points based on acoustic features. This segmentation allows the cascaded encoder to process shorter segments independently, reducing the overall processing time and latency while maintaining recognition accuracy through context-aware segmentation.
Solution Approach 2:
The first encoder performs preliminary processing of acoustic frames to generate intermediate representations before the second encoder processes them further. This preliminary action allows the system to start processing early segments while later segments are still being captured, reducing perceived latency without sacrificing accuracy.
2Productivity
If segmentation is integrated with cascaded encoding, then processing efficiency is improved, but deletion errors occur in transcribing speech
Solution Approach 1:
The system uses feedback from the first decoder's end-of-speech predictions to guide the second encoder's processing. The second encoder receives both the intermediate representations from the first encoder and the end-of-speech timestamps, allowing it to adjust processing and prevent deletion errors while maintaining high processing efficiency through coordinated multi-pass processing.
3Loss of time
If real-time end-of-speech prediction is implemented, then finalization latency is reduced, but processing complexity increases
Solution Approach 1:
The model is segmented into two independent encoders and two decoders, where the first pass handles real-time end-of-speech prediction with lower complexity, and the second pass refines the transcription. This segmentation allows real-time finalization without requiring the entire system to be overly complex.
Solution Approach 2:
Both encoders and decoders are designed to perform multiple functions: the first encoder/decoder handle real-time processing and end-of-speech detection, while the second encoder/decoder perform refinement. This multi-functionality reduces overall system complexity by reusing similar architectural components across different processing stages.
Data Source
AI summary
A unified end-to-end segmenter and two-pass automatic speech recognition (ASR) model includes a first encoder, a first decoder, a second encoder, and a second decoder. The first encoder is configured to receive a sequence of acoustic frames and generate a first higher order feature representation. The first decoder is configured to receive the first higher order feature representation and generate, at each of a plurality of output steps, a first probability distribution and an indication of whether the output step corresponds to an end of speech segment, and emit an end of speech timestamp. The second encoder is configured to receive the first higher order feature representation and the end of speech timestamp, and generate a second higher order feature representation. The second decoder is configured to receive the second higher order feature representation and generate a second probability distribution.