# Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages

arXiv: https://arxiv.org/abs/2011.03646 · HTML: https://mlsanity.com/paper/2011.03646/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [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)
- [Decoding Emotions: A comprehensive Multilingual Study of Speech Models for Speech Emotion Recognition](https://mlsanity.com/paper/2308.08713/) (2023)
- [Bengali Intent Classification with Generative Adversarial BERT](https://mlsanity.com/paper/2312.10679/) (2023)
- [Word-Free Spoken Language Understanding for Mandarin-Chinese](https://mlsanity.com/paper/2107.00186/) (2021)
- [Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search](https://mlsanity.com/paper/2211.08237/) (2022)
- [Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages](https://mlsanity.com/paper/2110.09264/) (2021)
- [Creating Spoken Dialog Systems in Ultra-Low Resourced Settings](https://mlsanity.com/paper/2312.06266/) (2023)

## Abstract (arXiv)

With recent advancements in language technologies, humans are now speaking to devices. Increasing the reach of spoken language technologies requires building systems in local languages. A major bottleneck here are the underlying data-intensive parts that make up such systems, including automatic speech recognition (ASR) systems that require large amounts of labelled data. With the aim of aiding development of spoken dialog systems in low resourced languages, we propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification. The system is made up of two blocks - the first block is a universal phone recognition system that generates a transcript of discovered phonetic units for the input audio, and the second block performs intent classification from the generated phonetic transcripts. We propose a CNN+LSTM based architecture and present results for two languages families - Indic languages and Romance languages, for two different intent recognition tasks. We also perform multilingual training of our intent classifier and show improved cross-lingual transfer and zero-shot performance on an unknown language within the same language family.