CLSDASMar 11, 2025

ESPnet-SDS: Unified Toolkit and Demo for Spoken Dialogue Systems

NVIDIA
arXiv:2503.08533v110 citationsh-index: 19Has CodeNAACL
Originality Synthesis-oriented
AI Analysis

This toolkit helps researchers compare spoken dialogue systems more effectively, though it is incremental as it builds on existing audio foundation models.

The authors tackled the challenge of comparing different spoken dialogue systems by introducing an open-source toolkit that builds unified web interfaces and provides automated evaluation metrics, demonstrating that current end-to-end systems have poorer audio quality and less diverse responses.

Advancements in audio foundation models (FMs) have fueled interest in end-to-end (E2E) spoken dialogue systems, but different web interfaces for each system makes it challenging to compare and contrast them effectively. Motivated by this, we introduce an open-source, user-friendly toolkit designed to build unified web interfaces for various cascaded and E2E spoken dialogue systems. Our demo further provides users with the option to get on-the-fly automated evaluation metrics such as (1) latency, (2) ability to understand user input, (3) coherence, diversity, and relevance of system response, and (4) intelligibility and audio quality of system output. Using the evaluation metrics, we compare various cascaded and E2E spoken dialogue systems with a human-human conversation dataset as a proxy. Our analysis demonstrates that the toolkit allows researchers to effortlessly compare and contrast different technologies, providing valuable insights such as current E2E systems having poorer audio quality and less diverse responses. An example demo produced using our toolkit is publicly available here: https://huggingface.co/spaces/Siddhant/Voice_Assistant_Demo.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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