Factorized Neural Transducer Enables Text-Only ASR Adaptation
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Solution Overview
Problem
Conventional transducer-based ASR systems face challenges in adapting to new domains due to the computational expense of collecting new audio-data with corresponding speech labels, leading to decreased accuracy in the original domain and increased computational costs, and there is a need for more efficient methods to adapt these systems.
Innovation Solution
The factorized neural transducer architecture separates blank and vocabulary prediction layers, allowing the language model to be adapted independently, enabling faster and more efficient adaptation using text-only data, and incorporates techniques like CTC criterion, KL divergence, N-gram interpolation, and external language models to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire ASR system is adapted using new adaptation data to improve accuracy in the new domain, then the accuracy for new domain speech recognition is improved, but the computational cost and time for updating all layers increases significantly
Solution Approach 1:
The ASR system is segmented into multiple independent components: acoustic model, language model, and decoder. Only the language model is retrained on new adaptation data, while the acoustic model and decoder are frozen. This segmentation enables selective adaptation that reduces computational cost and time while maintaining accuracy improvements in the new domain.
2Measurement precision
If the entire ASR system is adapted using new adaptation data to improve accuracy in the new domain, then the accuracy for new domain speech recognition is improved, but the computational resources required for training all layers increases
Solution Approach 1:
The language model is extracted as a separate, independently trainable component from the rest of the ASR system. This extraction allows the language model to be retrained on new adaptation data using fewer computational resources, while the acoustic model and decoder are reused without retraining, significantly reducing overall computational cost.
3Measurement precision
If the ASR system is adapted to a new domain using robust adaptation data, then the accuracy for new domain speech recognition is improved, but the system decreases in accuracy for performing speech recognition in the baseline domain
Solution Approach 1:
Different parts of the system are assigned different roles: the acoustic model is optimized for baseline domain speech recognition and is frozen during adaptation, while the language model is retrained on new adaptation data to capture domain-specific linguistic patterns. This local quality assignment allows the system to maintain baseline domain accuracy while adapting to new domains.
4Measurement precision
If new audio data with corresponding speech labels is collected for adaptation to improve accuracy in the new domain, then the accuracy is improved, but the time and computational resources required for data collection and labeling increases
Solution Approach 1:
The system uses text-only adaptation data as a substitute for full audio-text paired data. By copying and adapting only the language model component with text data, the system achieves domain adaptation without requiring time-consuming audio data collection and manual speech labeling, significantly reducing data preparation time and costs.
Data Source
AI summary
Systems and methods are provided for accessing a factorized neural transducer comprising a first set of layers for predicting blank tokens and a second set of layers for predicting vocabulary tokens, the second set of layers comprising a language model that includes a vocabulary predictor which is a separate predictor from the blank predictor, wherein a vocabulary predictor output from the vocabulary predictor and the encoder output are used for predicting a vocabulary token. The second set of layers is selectively modified to facilitate an improvement in an accuracy of the factorized neural transducer in performing automatic speech recognition, the selectively modifying comprising applying a particular modification to the second set of layers while refraining from applying the particular modification to the first set of layers.