Speech Recognition Hypothesis Generation for Accent Adaptation
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Solution Overview
Problem
Existing speech recognition systems struggle with accuracy due to variations in user accents and speech differences, leading to misrecognition and inefficient use of computational resources.
Innovation Solution
Generate multiple speech recognition hypotheses, identify matching and inconsistent terms, and use term correspondence data to prioritize and adapt the recognition process to user accents, generating supplemental hypotheses to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If speech recognition processes attempt to adapt to certain accents by learning from explicitly identified errors, then speech recognition accuracy for those accents may improve, but processing time increases and computational resources are wasted
Solution Approach 1:
The system performs preliminary actions by generating multiple speech recognition hypotheses in advance and identifying term correspondences between them before actual speech processing occurs. This pre-computation of potential recognition variations allows the system to quickly adapt to accents without requiring time-consuming iterative learning from user corrections during actual use.
Solution Approach 2:
The system dynamically adapts speech recognition by selectively applying term correspondences from multiple hypotheses based on contextual relevance. Rather than using a static accent model, the system dynamically adjusts which hypothesis terms to prioritize based on the specific speech input and context, enabling flexible adaptation without exhaustive processing.
2Measurement precision
If multiple speech recognition hypotheses are generated and processed to account for accent variations, then speech recognition accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system extracts only the essential term correspondences from multiple speech recognition hypotheses rather than processing entire hypothesis sets. By identifying and isolating specific terms that differ across hypotheses and extracting their contextual relationships, the system reduces computational overhead while maintaining the benefits of multi-hypothesis analysis for accent adaptation.
Solution Approach 2:
The system applies local quality by focusing computational effort on specific terms within hypotheses that are most likely to contain accent-related variations. Rather than uniformly processing all terms in multiple hypotheses, the system identifies local regions (specific terms) that benefit most from multi-hypothesis analysis and applies term correspondences selectively to those areas.
3Measurement precision
If users repeatedly provide spoken utterances to correct recognition mistakes, then speech recognition accuracy may eventually improve, but user time and computational resources are wasted
Solution Approach 1:
The system implements feedback by using term correspondences identified from multiple speech recognition hypotheses to automatically adjust and improve recognition accuracy for future inputs. This internal feedback mechanism eliminates the need for users to repeatedly provide correction utterances, as the system proactively learns from the patterns across multiple hypotheses and applies those learnings to subsequent recognition tasks.
Data Source
AI summary
Implementations set forth herein relate to speech recognition techniques for handling variations in speech among users (e.g. due to different accents) and processing features of user context in order to expand a number of speech recognition hypotheses when interpreting a spoken utterance from a user. In order to adapt to an accent of the user, terms common to multiple speech recognition hypotheses can be filtered out in order to identify inconsistent terms apparent in a group of hypotheses. Mappings between inconsistent terms can be stored for subsequent users as term correspondence data. In this way, supplemental speech recognition hypotheses can be generated and subject to probability-based scoring for identifying a speech recognition hypothesis that most correlates to a spoken utterance provided by a user. In some implementations, prior to scoring, hypotheses can be supplemented based on contextual data, such as on-screen content and/or application capabilities.