Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Spoken language understanding (SLU) is an essential task for machines to understand human speech for better interactions. However, errors from the automatic speech recognizer (ASR) usually hurt the understanding performance. In reality, ASR systems may not be easy to adjust for the target scenarios. Therefore, this paper focuses on learning utterance representations that are robust to ASR errors using a contrastive objective, and further strengthens the generalization ability by combining supervised contrastive learning and self-distillation in model fine-tuning. Experiments on three benchmark datasets demonstrate the effectiveness of our proposed approach.
Code (1)
Tasks
Contrastive LearningSpoken Language UnderstandingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding
Spoken language understanding (SLU) is a fundamental task in the task-oriented dialogue systems. However, the inevitable errors from automatic speech recognition (ASR) usually impair the understanding performance and lea…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Contrastive Learningspeech-recognition+3Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning
Spoken language understanding (SLU) is a core task in task-oriented dialogue systems, which aims at understanding the user's current goal through constructing semantic frames. SLU usually consists of two subtasks, includ…
Contrastive LearningIntent Detectionslot-fillingSlot Filling+2Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understanding
Recently, deep end-to-end learning has been studied for intent classification in Spoken Language Understanding (SLU). However, end-to-end models require a large amount of speech data with intent labels, and highly optimi…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)intent-classificationIntent Classification+6HC$^2$L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding
State-of-the-art model for zero-shot cross-lingual spoken language understanding performs cross-lingual unsupervised contrastive learning to achieve the label-agnostic semantic alignment between each utterance and its co…
Contrastive LearningSpoken Language UnderstandingPre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning
In the traditional cascading architecture for spoken language understanding (SLU), it has been observed that automatic speech recognition errors could be detrimental to the performance of natural language understanding. …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+6