Spoken Dialogue Systems
19 papers with code • 0 benchmarks • 2 datasets
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Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model
This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules.
OLISIA: a Cascade System for Spoken Dialogue State Tracking
Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language. In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model.
When can I Speak? Predicting initiation points for spoken dialogue agents
Current spoken dialogue systems initiate their turns after a long period of silence (700-1000ms), which leads to little real-time feedback, sluggish responses, and an overall stilted conversational flow.
EVI: Multilingual Spoken Dialogue Tasks and Dataset for Knowledge-Based Enrolment, Verification, and Identification
Knowledge-based authentication is crucial for task-oriented spoken dialogue systems that offer personalised and privacy-focused services.
"How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations
Most prior work in dialogue modeling has been on written conversations mostly because of existing data sets.
Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Hierarchical Multi-Task Natural Language Understanding for Cross-domain Conversational AI: HERMIT NLU
We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems.
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task.
Variational Cross-domain Natural Language Generation for Spoken Dialogue Systems
Cross-domain natural language generation (NLG) is still a difficult task within spoken dialogue modelling.