Adaptive Boosting for Speech Recognition Personalization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Transformer-based end-to-end speech recognition models trained using joint CTC-attention loss face challenges in effectively personalizing CTC models for domain-specific vocabularies, leading to difficulties in accurately recognizing rare and out-of-vocabulary words due to conditional independence assumptions and output complexities such as blanks and duplicate tokens.
Innovation Solution
The proposed solution involves adaptive boosting for sub-word biasing, a phone alignment network to improve rare word recognition, and the use of grapheme-to-grapheme (G2G) techniques to generate additional pronunciation variants, combined with a conformer CTC-attention framework and dynamic adaptive boosting in beam search decoding to enhance inference speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If transformer-based end-to-end speech recognition models trained using joint CTC-attention loss are used, then speech recognition speed and simplicity are improved, but personalization toward domain-specific vocabulary deteriorates
Solution Approach 1:
The vocabulary is segmented into frequent and rare words, with different handling strategies applied to each segment. Frequent words are processed through the standard CTC decoder, while rare words receive specialized treatment through phone alignment networks and boosted beam search, allowing the system to maintain speed for common words while improving personalization for domain-specific vocabulary.
Solution Approach 2:
Different decoding strategies are applied to different parts of the vocabulary based on their frequency characteristics. The system applies phone alignment and boosted beam search specifically to rare words rather than uniformly to all vocabulary, optimizing both speed and personalization by tailoring the approach to local vocabulary characteristics.
2Ease of operation
If standard CTC decoding is used, then decoding simplicity is improved, but recognition accuracy for rare and out-of-vocabulary words deteriorates
Solution Approach 1:
A phone alignment network is introduced as an intermediary component between the acoustic model and the vocabulary. This network learns phone-level alignments that bridge the gap between acoustic features and rare words, providing intermediate structural information that improves rare word recognition without complicating the overall decoding process.
Solution Approach 2:
The system performs preliminary phone alignment and sub-word boosting before final word decoding. By pre-processing the acoustic representations through phone alignment networks and applying boosted beam search for rare words before the final CTC decoding step, the system improves accuracy for rare words while maintaining the simplicity of the overall framework.
3Measurement precision
If phone alignment network and adaptive boosting are applied, then rare word recognition accuracy is improved, but computational complexity increases
Solution Approach 1:
The system applies phone alignment networks and adaptive boosting selectively only to rare words rather than to the entire vocabulary. This partial application reduces the overall computational burden while still achieving improved rare word recognition accuracy, avoiding the excessive complexity that would result from applying these techniques uniformly to all words.
Data Source
AI summary
Techniques for personalized batch and streaming speech-to-text transcription of audio reduce the error rate of automatic speech recognition (ASR) systems in transcribing rare and out-of-vocabulary words. The techniques achieve personalization of connectionist temporal classification (CT) models by using adaptive boosting to perform biasing at the level of sub-words. In addition to boosting, the techniques encompass a phone alignment network to bias sub-word predictions towards rare long-tail words and out-of-vocabulary words. A technical benefit of the techniques is that the accuracy of speech-to-text transcription of rare and out-of-vocabulary words in a custom vocabulary by automatic speech recognition (ASR) system can be improved without having to train the ASR system on the custom vocabulary. Instead, the techniques allow the same ASR system trained on a base vocabulary to realize the accuracy improvements for different custom vocabularies spanning different domains.