6 accepted papers
We introduce Trustera <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , the first functional system that redacts personally identifiable information (PII) in real-time spoken conversations to remove agents’ need to hear sensitive information whi…
Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar…
Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. However, these models are often created from scratch, for specific clients and use cases and require the annotation of large d…
While human language provides a natural interface for humanmachine communication, there are several challenges concerning extracting the intents of a speaker when interacting with a virtual agent, especially when the speaker is in a noisy acoustic environment, that still remains to be solved. In thi…
While conventional approaches to spoken language understanding involve cascading a speech recognizer with a language understanding system, in this paper, we describe a novel approach for deriving semantics directly from the speech signal without the need for an explicit speech recognition step. We e…
Intonational phrase (IP) break prediction is an important aspect of front-end analysis in a text-to-speech system. Standard approaches for intonational phrase break prediction rely on the use of linguistic rules or more recently, lexicalized data-driven models. Linguistic rules are not robust while…