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. We present a domain-aware rescoring framework suitable for achieving domain-adaptation during second-pass rescoring in production settings. In our framework, we fine-tune a domain-general neural language model on several domains, and use an LSTM-based domain classification model to select the appropriate domain-adapted model to use for second-pass rescoring. This domain-aware rescoring improves the word error rate by up to 2.4% and slot word error rate by up to 4.1% on three individual domains -- shopping, navigation, and music -- compared to domain general rescoring. These improvements are obtained while maintaining accuracy for the general use case.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain Adaptationdomain classificationLanguage ModelingLanguage Modellingspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastiv…
Multi-Task LearningRepresentation LearningSelf-Supervised Learningspeech-recognition+3Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific contextual information in various applicati…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+3Enhancing ASR Performance in the Medical Domain for Dravidian Languages
Automatic Speech Recognition (ASR) for low-resource Dravidian languages like Telugu and Kannada faces significant challenges in specialized medical domains due to limited annotated data and morphological complexity. This…
Speech RecognitionMoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition
Multi-lingual speech recognition aims to distinguish linguistic expressions in different languages and integrate acoustic processing simultaneously. In contrast, current multi-lingual speech recognition research follows …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech RecognitionLaSR: Context-Aware Speech Recognition via Latent Reasoning
Recent advances in Speech Large Language Models (Speech LLMs) have significantly enhanced spoken language understanding and reasoning. However, their contextual awareness is limited, struggling to perform speech recognit…
Spoken Language UnderstandingSpeech Recognition