mlsanityData to 7 Oct 2026

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

arXiv 2503.08533 ↗First posted 11 Mar 2025cs.CL cs.SD eess.AScited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

24 of 43 references.

References · 24

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
  3. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,151
  4. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 554
  5. 2024Moshi: a speech-text foundation model for real-time dialoguecited by 536
  6. 2023SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilitiescited by 518
  7. 2018Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacescited by 376
  8. 2021Generative Spoken Language Modeling from Raw Audiocited by 336
  9. 2023AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Headcited by 264
  10. 2020DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressorscited by 235
  11. 2024Mini-Omni: Language Models Can Hear, Talk While Thinking in Streamingcited by 204
  12. 2021DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressorscited by 165
12 more
  1. 2020Few-shot Natural Language Generation for Task-Oriented Dialogcited by 89
  2. 2020DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialoguecited by 89
  3. 2024OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformercited by 74
  4. 2024Language Model Can Listen While Speakingcited by 62
  5. 2019ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkitcited by 60
  6. 2024Distilling an End-to-End Voice Assistant Without Instruction Training Datacited by 35
  7. 2024OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identificationcited by 34
  8. 2024ESPnet-Codec: Comprehensive Training and Evaluation of Neural Codecs for Audio, Music, and Speechcited by 25
  9. 2024Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusioncited by 20
  10. 2024MOS-Bench: Benchmarking Generalization Abilities of Subjective Speech Quality Assessment Modelscited by 11
  11. 2023PLCMOS -- a data-driven non-intrusive metric for the evaluation of packet loss concealment algorithmscited by 7
  12. 2023Joint Modelling of Spoken Language Understanding Tasks with Integrated Dialog Historycited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.