Personalization for BERT-based Discriminative Speech Recognition Rescoring
Recognition of personalized content remains a challenge in end-to-end speech recognition. We explore three novel approaches that use personalized content in a neural rescoring step to improve recognition: gazetteers, prompting, and a cross-attention based encoder-decoder model. We use internal de-identified en-US data from interactions with a virtual voice assistant supplemented with personalized named entities to compare these approaches. 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. We also show that on this test set, natural language prompts can improve word error rate by 7% without any training and with a marginal loss in generalization. Overall, gazetteers were found to perform the best with a 10% improvement in word error rate (WER), while also improving WER on a general test set by 1%.
Code (0)
등록된 구현이 없습니다.
Tasks
Decoderspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT
Second-pass rescoring is an important component in automatic speech recognition (ASR) systems that is used to improve the outputs from a first-pass decoder by implementing a lattice rescoring or $n$-best re-ranking. Whil…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)DecoderLanguage Modelling+4Low-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. Although pretrained language models (LMs) like BERT have shown superior performance in second-pass…
Language ModelingLanguage ModellingLarge Language Modelspeech-recognition+1Scaling Laws for Discriminative Speech Recognition Rescoring Models
Recent studies have found that model performance has a smooth power-law relationship, or scaling laws, with training data and model size, for a wide range of problems. These scaling laws allow one to choose nearly optima…
speech-recognitionSpeech RecognitionDistillation Strategies for Discriminative Speech Recognition Rescoring
Second-pass rescoring is employed in most state-of-the-art speech recognition systems. Recently, BERT based models have gained popularity for re-ranking the n-best hypothesis by exploiting the knowledge from masked langu…
Language ModelingLanguage ModellingRe-Rankingspeech-recognition+1Discriminative Speech Recognition Rescoring with Pre-trained Language Models
Second pass rescoring is a critical component of competitive automatic speech recognition (ASR) systems. Large language models have demonstrated their ability in using pre-trained information for better rescoring of ASR …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition