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Multi-Modal Retrieval For Large Language Model Based Speech Recognition
Retrieval is a widely adopted approach for improving language models leveraging external information.
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition
We propose a neural language modeling system based on low-rank adaptation (LoRA) for speech recognition output rescoring.
Personalization for BERT-based Discriminative Speech Recognition Rescoring
On a test set with personalized named entities, we show that each of these approaches improves word error rate by over 10%, against a neural rescoring baseline.
On-the-fly Text Retrieval for End-to-End ASR Adaptation
End-to-end speech recognition models are improved by incorporating external text sources, typically by fusion with an external language model.
Personalization Strategies for End-to-End Speech Recognition Systems
We also describe a novel second-pass de-biasing approach: used in conjunction with a first-pass shallow fusion that optimizes on oracle WER, we can achieve an additional 14% improvement on personalized content recognition, and even improve accuracy for the general use case by up to 2. 5%.
Domain-aware Neural Language Models for Speech Recognition
As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains.
