# Creating Spoken Dialog Systems in Ultra-Low Resourced Settings

arXiv: https://arxiv.org/abs/2312.06266 · HTML: https://mlsanity.com/paper/2312.06266/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey](https://mlsanity.com/paper/2011.00564/) (2020)
- [MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition](https://mlsanity.com/paper/2102.12664/) (2021)
- [Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models](https://mlsanity.com/paper/1910.09463/) (2019)
- [Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages](https://mlsanity.com/paper/2011.03646/) (2020)
- [Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communities](https://mlsanity.com/paper/2010.16411/) (2020)
- [Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems](https://mlsanity.com/paper/2104.01287/) (2021)
- [On Building Spoken Language Understanding Systems for Low Resourced Languages](https://mlsanity.com/paper/2205.12818/) (2022)
- [A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots](https://mlsanity.com/paper/2111.01414/) (2021)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) systems are a crucial technology that is used today to design a wide variety of applications, most notably, smart assistants, such as Alexa. ASR systems are essentially dialogue systems that employ Spoken Language Understanding (SLU) to extract meaningful information from speech. The main challenge with designing such systems is that they require a huge amount of labeled clean data to perform competitively, such data is extremely hard to collect and annotate to respective SLU tasks, furthermore, when designing such systems for low resource languages, where data is extremely limited, the severity of the problem intensifies. In this paper, we focus on a fairly popular SLU task, that is, Intent Classification while working with a low resource language, namely, Flemish. Intent Classification is a task concerned with understanding the intents of the user interacting with the system. We build on existing light models for intent classification in Flemish, and our main contribution is applying different augmentation techniques on two levels -- the voice level, and the phonetic transcripts level -- to the existing models to counter the problem of scarce labeled data in low-resource languages. We find that our data augmentation techniques, on both levels, have improved the model performance on a number of tasks.