  A major focus of recent research in spoken language understanding (SLU) has
been on the end-to-end approach where a single model can predict intents
directly from speech inputs without intermediate transcripts. However, this
approach presents some challenges. First, since speech can be considered as
personally identifiable information, in some cases only automatic speech
recognition (ASR) transcripts are accessible. Second, intent-labeled speech
data is scarce. To address the first challenge, we propose a novel system that
can predict intents from flexible types of inputs: speech, ASR transcripts, or
both. We demonstrate strong performance for either modality separately, and
when both speech and ASR transcripts are available, through system combination,
we achieve better results than using a single input modality. To address the
second challenge, we leverage a semantically robust pre-trained BERT model and
adopt a cross-modal system that co-trains text embeddings and acoustic
embeddings in a shared latent space. We further enhance this system by
utilizing an acoustic module pre-trained on LibriSpeech and domain-adapting the
text module on our target datasets. Our experiments show significant advantages
for these pre-training and fine-tuning strategies, resulting in a system that
achieves competitive intent-classification performance on Snips SLU and Fluent
Speech Commands datasets.

Computation and Language;Machine Learning;Sound;Audio and Speech Processing