activity
20192022
most citedBenchmarking Natural Language Understanding Services for building Conversational Agents

89 citations · 96 across the 4 of their papers we have counts for

collaborators

6 papers

cs.SD20221 cited

Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation

Thien Nguyen, Nathalie Tran, Liuhui Deng +16

Code-switching describes the practice of using more than one language in the same sentence. In this study, we investigate how to optimize a neural transducer based bilingual automa…

cs.CL20201 cited

SLURP: A Spoken Language Understanding Resource Package

Emanuele Bastianelli, Andrea Vanzo, Pawel Swietojanski +1

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. Howeve…

cs.HC20205 cited

Building Proactive Voice Assistants: When and How (not) to Interact

O. Miksik, I. Munasinghe, J. Asensio-Cubero +17

Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other…

eess.AS2020

Multi-task self-supervised learning for Robust Speech Recognition

Mirco Ravanelli, Jianyuan Zhong, Santiago Pascual +4

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recentl…

cs.SD2019

Static Visual Spatial Priors for DoA Estimation

Pawel Swietojanski, Ondrej Miksik

As we interact with the world, for example when we communicate with our colleagues in a large open space or meeting room, we continuously analyse the surrounding environment and, i…

cs.CL201989 cited

Benchmarking Natural Language Understanding Services for building Conversational Agents

Xingkun Liu, Arash Eshghi, Pawel Swietojanski +1

We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogu…