Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR

• URL: http://arxiv.org/abs/2210.12134v1
• Date: Fri, 21 Oct 2022 17:45:00 GMT
• Title: Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR
• Authors: Pranay Dighe, Prateeth Nayak, Oggi Rudovic, Erik Marchi, Xiaochuan Niu, Ahmed Tewfik
• Abstract summary: We present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens. We show that our approach is highly accurate with correctly mitigating 93.3% of unintended user audio from invoking the smart assistant at 99% true positive rate.
• Score: 8.832255053182283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of the user intent to interact with a voice assistant (VA) on a device (e.g. on the phone) is critical for achieving naturalistic, engaging, and privacy-centric interactions with the VA. To this end, we present a novel approach to predict the user's intent (the user speaking to the device or not) directly from acoustic and textual information encoded at subword tokens which are obtained via an end-to-end ASR model. Modeling directly the subword tokens, compared to modeling of the phonemes and/or full words, has at least two advantages: (i) it provides a unique vocabulary representation, where each token has a semantic meaning, in contrast to the phoneme-level representations, (ii) each subword token has a reusable "sub"-word acoustic pattern (that can be used to construct multiple full words), resulting in a largely reduced vocabulary space than of the full words. To learn the subword representations for the audio-to-intent classification, we extract: (i) acoustic information from an E2E-ASR model, which provides frame-level CTC posterior probabilities for the subword tokens, and (ii) textual information from a pre-trained continuous bag-of-words model capturing the semantic meaning of the subword tokens. The key to our approach is the way it combines acoustic subword-level posteriors with text information using the notion of positional-encoding in order to account for multiple ASR hypotheses simultaneously. We show that our approach provides more robust and richer representations for audio-to-intent classification, and is highly accurate with correctly mitigating 93.3% of unintended user audio from invoking the smart assistant at 99% true positive rate.

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