Multimodal Audio-textual Architecture for Robust Spoken Language Understanding

TL;DR

This paper introduces a multimodal audio-textual architecture that enhances spoken language understanding by reducing errors propagated from automatic speech recognition, leveraging self-supervised features from both modalities.

Contribution

The work proposes a novel multimodal language understanding module combining audio and text encoders with late fusion, improving robustness against ASR errors in SLU tasks.

Findings

MLU outperforms pure PLM models on multiple SLU datasets.

The approach is robust to poor-quality ASR transcripts.

It surpasses existing models in accuracy across various ASR engines.

Abstract

Recent voice assistants are usually based on the cascade spoken language understanding (SLU) solution, which consists of an automatic speech recognition (ASR) engine and a natural language understanding (NLU) system. Because such approach relies on the ASR output, it often suffers from the so-called ASR error propagation. In this work, we investigate impacts of this ASR error propagation on state-of-the-art NLU systems based on pre-trained language models (PLM), such as BERT and RoBERTa. Moreover, a multimodal language understanding (MLU) module is proposed to mitigate SLU performance degradation caused by errors present in the ASR transcript. The MLU benefits from self-supervised features learned from both audio and text modalities, specifically Wav2Vec for speech and Bert/RoBERTa for language. Our MLU combines an encoder network to embed the audio signal and a text encoder to process text transcripts followed by a late fusion layer to fuse audio and text logits. We found that the proposed MLU showed to be robust towards poor quality ASR transcripts, while the performance of BERT and RoBERTa are severely compromised. Our model is evaluated on five tasks from three SLU datasets and robustness is tested using ASR transcripts from three ASR engines. Results show that the proposed approach effectively mitigates the ASR error propagation problem, surpassing the PLM models' performance across all datasets for the academic ASR engine.