Network-Based Hypothesis Stitching for Long-Form Speech Recognition
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Solution Overview
Problem
End-to-end automatic speech recognition systems underperform when applied to long-form speech exceeding training data length, leading to reduced accuracy and increased computational costs.
Innovation Solution
Segment audio streams into multiple segments, identify speakers within each segment, perform ASR to generate short-segment hypotheses, merge these hypotheses using stitching symbols, and consolidate them into a single hypothesis using a network-based stitcher, employing alignment-based or serialized stitchers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If end-to-end ASR systems process long-form audio, then speech recognition accuracy improves, but computational cost increases and system complexity increases
Solution Approach 1:
The patent segments long-form audio into multiple shorter segments (e.g., 10-second windows with overlap), processes each segment independently through the ASR model, and then stitches the results together. This segmentation allows the system to handle long-form audio using a simpler model trained on short utterances, resolving the contradiction between accuracy for long audio and system complexity.
Solution Approach 2:
The patent merges short-segment hypotheses into a consolidated hypothesis using a stitching mechanism that aligns and combines the segmented transcriptions. This merging process reconstructs the full long-form transcription from multiple short segments, achieving accurate long-form recognition without requiring a complex end-to-end model.
2Measurement precision
If end-to-end ASR systems process long-form audio, then speech recognition accuracy improves, but computational cost increases
Solution Approach 1:
By segmenting long audio into shorter segments, the system can use a simpler, more efficient ASR model trained on short utterances rather than requiring a complex end-to-end model. This segmentation approach reduces computational cost per processing step while maintaining overall accuracy through the stitching mechanism.
Solution Approach 2:
The system processes only the necessary portions of audio (short segments) at a time rather than processing the entire long-form audio continuously. This partial processing approach reduces computational cost while the stitching mechanism ensures complete and accurate transcription is achieved through combining the partial results.
3Device complexity
If audio is segmented into short segments for ASR processing, then computational cost decreases, but speech recognition accuracy for long-form audio deteriorates
Solution Approach 1:
The patent merges short-segment hypotheses into a consolidated hypothesis using alignment-based or serialized stitching methods. This merging process combines the transcriptions from multiple short segments into a coherent long-form transcription, restoring speech recognition accuracy that would otherwise be lost due to the segmentation itself.
Solution Approach 2:
The stitching mechanism incorporates feedback loops that align and verify the segmented hypotheses against each other, using overlap regions to ensure consistency. This feedback process corrects potential inaccuracies introduced by segmentation and maintains high speech recognition accuracy in the final consolidated output.
Data Source
AI summary
A hypothesis stitcher for speech recognition of long-form audio provides superior performance, such as higher accuracy and reduced computational cost. An example disclosed operation includes: segmenting the audio stream into a plurality of audio segments; identifying a plurality of speakers within each of the plurality of audio segments; performing automatic speech recognition (ASR) on each of the plurality of audio segments to generate a plurality of short-segment hypotheses; merging at least a portion of the short-segment hypotheses into a first merged hypothesis set; inserting stitching symbols into the first merged hypothesis set, the stitching symbols including a window change (WC) symbol; and consolidating, with a network-based hypothesis stitcher, the first merged hypothesis set into a first consolidated hypothesis. Multiple variations are disclosed, including alignment-based stitchers and serialized stitchers, which may operate as speaker-specific stitchers or multi-speaker stitchers, and may further support multiple options for differing hypothesis configurations.