Context-Aware Beam Search for Proper Noun Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
End-to-end speech recognition models struggle with accurately recognizing out-of-vocabulary words and proper nouns due to limited training data and efficient decoding processes that prune rare words early, leading to poor accuracy in contextual biasing.
Innovation Solution
Incorporate contextual information into the decoding process of end-to-end models by using weighted finite state transducers to bias speech recognition towards relevant words and phrases, applying context scores before beam pruning, and training with large text datasets to improve proper noun recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If end-to-end models use beam search decoding to improve decoding efficiency, then decoding speed improves, but recognition accuracy of rare words and proper nouns deteriorates due to early pruning
Solution Approach 1:
The system performs preliminary actions by generating context scores from context data before the beam search decoding process begins. These context scores are used to adjust the initial probabilities of candidate words, ensuring that rare words and proper nouns relevant to the current context are not prematurely pruned during beam search. This preliminary biasing preserves important candidates that would otherwise be eliminated by standard beam pruning.
Solution Approach 2:
The system introduces context scores as an intermediary mechanism between the context data and the beam search decoder. These context scores act as a mediator that modulates the probability distribution over candidate words during decoding, allowing rare and proper nouns to maintain competitive scores despite their low frequency in general training data. The context scores bridge the gap between contextual relevance and decoding probability.
2Adaptability or versatility
If end-to-end models are trained with joint acoustic, pronunciation, and language models to improve integration, then model unity improves, but proper noun recognition deteriorates due to limited training data
Solution Approach 1:
The system applies local quality by generating context-specific scores for proper nouns and rare words based on the current context data. Instead of relying solely on the globally trained end-to-end model, the system locally adjusts probabilities for specific word classes (proper nouns, rare words) that are contextually relevant. This localized biasing compensates for the model's general weakness in these areas while maintaining the benefits of joint training.
Solution Approach 2:
The system changes parameters by introducing context scores that modify the probability distribution over candidate words during decoding. These context scores effectively change the likelihood parameters for proper nouns and rare words based on contextual relevance, allowing the model to adapt its behavior for specific word classes without retraining the entire end-to-end system.
3Measurement precision
If conventional ASR systems use independent contextual language models with n-gram WFST to improve contextual biasing, then context relevance improves, but system complexity increases
Solution Approach 1:
The system merges the contextual language model functionality directly into the end-to-end decoding process. Instead of maintaining separate independent contextual LMs and composing them with baseline LMs using n-gram WFST, the system integrates context score generation and application within the unified end-to-end decoder. This merging reduces architectural complexity while preserving contextual biasing capabilities.
Solution Approach 2:
The system achieves universality by using a single end-to-end model that can handle both general speech recognition and contextual biasing for proper nouns and rare words. The context score mechanism provides multi-functional support, working across different contexts and word types without requiring separate specialized models. This universal approach simplifies the system compared to conventional multi-component architectures.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method (500) includes receiving audio data (125) encoding an utterance (120), processing, using a speech recognition model (220), the audio data to generate speech recognition scores (145) for speech elements, and determining context scores (166) for the speech elements based on context data indicating a context (122) for the utterance. The method also includes executing, using the speech recognition scores and the context scores, a beam search decoding process (180) to determine one or more candidate transcriptions for the utterance. The method also includes selecting a transcription (185) for the utterance from the one or more candidate transcriptions.