mlsanityData to 7 Oct 2026

Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models

arXiv 1910.09463 ↗First posted 21 Oct 2019eess.AScited by 24

Not on the map. Why?

Abstract (arXiv)

End-to-end models are an attractive new approach to spoken language understanding (SLU) in which the meaning of an utterance is inferred directly from the raw audio without employing the standard pipeline composed of a separately trained speech recognizer and natural language understanding module. The downside of end-to-end SLU is that in-domain speech data must be recorded to train the model. In this paper, we propose a strategy for overcoming this requirement in which speech synthesis is used to generate a large synthetic training dataset from several artificial speakers. Experiments on two open-source SLU datasets confirm the effectiveness of our approach, both as a sole source of training data and as a form of data augmentation.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Oct 2019.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 9; 2022: 8; 2023: 6; 2024: 1.

24 papers.

Sort:

  1. 2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
  2. 2021Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understandingcited by 22
  3. 2022Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systemscited by 20
  4. 2021End-to-End Spoken Language Understanding for Generalized Voice Assistantscited by 17
  5. 2021Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classificationcited by 11
  6. 2021Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numberscited by 11
  7. 2021Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understandingcited by 11
  8. 2022End-to-end model for named entity recognition from speech without paired training datacited by 11
  9. 2021RNN Transducer Models For Spoken Language Understandingcited by 7
  10. 2022On the Use of Semantically-Aligned Speech Representations for Spoken Language Understandingcited by 7
  11. 2021Attentive Contextual Carryover for Multi-Turn End-to-End Spoken Language Understandingcited by 5
  12. 2023Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Trainingcited by 5
12 more
  1. 2023On the Transferability of Whisper-based Representations for "In-the-Wild" Cross-Task Downstream Speech Applicationscited by 4
  2. 2022End-to-End Spoken Language Understanding: Performance analyses of a voice command task in a low resource settingcited by 3
  3. 2022Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelscited by 2
  4. 2023Semantic enrichment towards efficient speech representationscited by 2
  5. 2024A dual task learning approach to fine-tune a multilingual semantic speech encoder for Spoken Language Understandingcited by 1
  6. 2021Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classificationcited by 0
  7. 2022A Study of Different Ways to Use The Conformer Model For Spoken Language Understandingcited by 0
  8. 2022Meta Auxiliary Learning for Low-resource Spoken Language Understandingcited by 0
  9. 2022Jaco: An Offline Running Privacy-aware Voice Assistantcited by 0
  10. 2023GRASS: Unified Generation Model for Speech-to-Semantic Taskscited by 0
  11. 2023Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesiscited by 0
  12. 2023Creating Spoken Dialog Systems in Ultra-Low Resourced Settingscited by 0