New Spoken Language Understanding (SLU) research from MIT CSAIL and Amazon AI introduces step-skipping semi-supervised frameworks that take speech as input and achieve performance competitive to systems leveraging oracle text.
SLU is commonly used nowadays as a frontend technology in device voice assistants and social bots. The SLU pipeline typically starts with an Automatic Speech Recognition (ASR) stage that maps audio to text, and ends with Natural Language Understanding (NLU) mapping the text to semantic slots.
The neural networks that power SLU frameworks however require large amounts of labelled training data, which is expensive and can often be difficult or impossible to collect. The proposed end-to-end SLU framework learns to map audio to semantics directly through semi-supervised learning.
Semi-supervised learning basically pretrains model components on large amounts of unlabelled data, then fine-tunes using target semantic labels. Although this approach has been implemented in previous studies, these efforts have had significant limitations:
- Models need a separate ASR or feedback from ASR
- Models are not designed to predict slot values
- The generalization function of self-supervised language models is not properly utilized
The researchers also identified several challenges with current SOTA models:
- Model training is difficult under limited labels
- Model noise-robustness is not trained or evaluated in real life environments
- Model evaluation lacks end-to-end intent classification
The proposed semi-supervised E2E learning framework uses ASR and a BERT language model pretrained on audio-text pairs for joint intent classification (IC) to perform slot labelling (SL) directly from speech under limited labels. The framework was also trained with explicit noise augmentation to make it robust to environmental noises.
In experiments with two public SLU corpora using the new E2E evaluation metric, the proposed framework showed comparable results on slots edit F1 score. The researchers say this is the first time an SLU model with speech as input was shown to perform on par with NLU models, and suggest future research directions could involve developing the framework’s multilingual SLU capabilities.
The paper Towards Semi-Supervised Semantics Understanding from Speech was presented in a Self-Supervised Learning for Speech and Audio Processing Workshop at NeurIPS 2020, and is available on arXiv.
Analyst: Reina Qi Wan | Editor: Michael Sarazen; Yuan Yuan
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