Device Directedness with Contextual Cues for Spoken Dialog Systems

• URL: http://arxiv.org/abs/2211.13280v1
• Date: Wed, 23 Nov 2022 19:49:11 GMT
• Title: Device Directedness with Contextual Cues for Spoken Dialog Systems
• Authors: Dhanush Bekal, Sundararajan Srinivasan, Sravan Bodapati, Srikanth Ronanki, Katrin Kirchhoff
• Abstract summary: We define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins. We use low-level speech representations from a self-supervised representation learning model for our downstream classification task. We propose a novel technique to infuse lexical information directly into speech representations to improve the domain-specific language information implicitly learned during pre-training.
• Score: 15.96415881820669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we define barge-in verification as a supervised learning task where audio-only information is used to classify user spoken dialogue into true and false barge-ins. Following the success of pre-trained models, we use low-level speech representations from a self-supervised representation learning model for our downstream classification task. Further, we propose a novel technique to infuse lexical information directly into speech representations to improve the domain-specific language information implicitly learned during pre-training. Experiments conducted on spoken dialog data show that our proposed model trained to validate barge-in entirely from speech representations is faster by 38% relative and achieves 4.5% relative F1 score improvement over a baseline LSTM model that uses both audio and Automatic Speech Recognition (ASR) 1-best hypotheses. On top of this, our best proposed model with lexically infused representations along with contextual features provides a further relative improvement of 5.7% in the F1 score but only 22% faster than the baseline.

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