Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining
This work addresses SLU for speech-based systems, offering a general framework that is incremental in combining existing pretrained models.
The paper tackles the problem of spoken language understanding (SLU) by proposing a semi-supervised framework that learns semantics directly from speech, addressing issues like ASR errors and limited labeled data. Experiments on ATIS show it performs on par with oracle text input in semantics understanding, even with environmental noise and limited training data.
Much recent work on Spoken Language Understanding (SLU) is limited in at least one of three ways: models were trained on oracle text input and neglected ASR errors, models were trained to predict only intents without the slot values, or models were trained on a large amount of in-house data. In this paper, we propose a clean and general framework to learn semantics directly from speech with semi-supervision from transcribed or untranscribed speech to address these issues. Our framework is built upon pretrained end-to-end (E2E) ASR and self-supervised language models, such as BERT, and fine-tuned on a limited amount of target SLU data. We study two semi-supervised settings for the ASR component: supervised pretraining on transcribed speech, and unsupervised pretraining by replacing the ASR encoder with self-supervised speech representations, such as wav2vec. In parallel, we identify two essential criteria for evaluating SLU models: environmental noise-robustness and E2E semantics evaluation. Experiments on ATIS show that our SLU framework with speech as input can perform on par with those using oracle text as input in semantics understanding, even though environmental noise is present and a limited amount of labeled semantics data is available for training.