# Improving End-to-End Speech-to-Intent Classification with Reptile

arXiv: https://arxiv.org/abs/2008.01994 · HTML: https://mlsanity.com/paper/2008.01994/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet](https://mlsanity.com/paper/2111.14706/) (2021)
- [SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks](https://mlsanity.com/paper/2303.00733/) (2023)
- [Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systems](https://mlsanity.com/paper/2203.00006/) (2022)
- [End-to-End Spoken Language Understanding for Generalized Voice Assistants](https://mlsanity.com/paper/2106.09009/) (2021)
- [SpeechPrompt: Prompting Speech Language Models for Speech Processing Tasks](https://mlsanity.com/paper/2408.13040/) (2024)
- [Integrating Dialog History into End-to-End Spoken Language Understanding Systems](https://mlsanity.com/paper/2108.08405/) (2021)
- [UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions](https://mlsanity.com/paper/2310.02973/) (2023)
- [A Streaming End-to-End Framework For Spoken Language Understanding](https://mlsanity.com/paper/2105.10042/) (2021)
- [FANS: Fusing ASR and NLU for on-device SLU](https://mlsanity.com/paper/2111.00400/) (2021)
- [RNN Transducer Models For Spoken Language Understanding](https://mlsanity.com/paper/2104.03842/) (2021)
- [Sequential End-to-End Intent and Slot Label Classification and Localization](https://mlsanity.com/paper/2106.04660/) (2021)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text models](https://mlsanity.com/paper/2206.14589/) (2022)
- [Device Directedness with Contextual Cues for Spoken Dialog Systems](https://mlsanity.com/paper/2211.13280/) (2022)
- [Meta Auxiliary Learning for Low-resource Spoken Language Understanding](https://mlsanity.com/paper/2206.12774/) (2022)
- [A Quantum Kernel Learning Approach to Acoustic Modeling for Spoken Command Recognition](https://mlsanity.com/paper/2211.01263/) (2022)
- [Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis](https://mlsanity.com/paper/2310.05374/) (2023)

## Abstract (arXiv)

End-to-end spoken language understanding (SLU) systems have many advantages over conventional pipeline systems, but collecting in-domain speech data to train an end-to-end system is costly and time consuming. One question arises from this: how to train an end-to-end SLU with limited amounts of data? Many researchers have explored approaches that make use of other related data resources, typically by pre-training parts of the model on high-resource speech recognition. In this paper, we suggest improving the generalization performance of SLU models with a non-standard learning algorithm, Reptile. Though Reptile was originally proposed for model-agnostic meta learning, we argue that it can also be used to directly learn a target task and result in better generalization than conventional gradient descent. In this work, we employ Reptile to the task of end-to-end spoken intent classification. Experiments on four datasets of different languages and domains show improvement of intent prediction accuracy, both when Reptile is used alone and used in addition to pre-training.