# Word-Free Spoken Language Understanding for Mandarin-Chinese

arXiv: https://arxiv.org/abs/2107.00186 · HTML: https://mlsanity.com/paper/2107.00186/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 10 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [Universal Phone Recognition with a Multilingual Allophone System](https://mlsanity.com/paper/2002.11800/) (2020)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [End-to-End Neural Transformer Based Spoken Language Understanding](https://mlsanity.com/paper/2008.10984/) (2020)
- [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)

## Built on by (most cited first, 1 of 1)

- [On Building Spoken Language Understanding Systems for Low Resourced Languages](https://mlsanity.com/paper/2205.12818/) (2022)

## Abstract (arXiv)

Spoken dialogue systems such as Siri and Alexa provide great convenience to people's everyday life. However, current spoken language understanding (SLU) pipelines largely depend on automatic speech recognition (ASR) modules, which require a large amount of language-specific training data. In this paper, we propose a Transformer-based SLU system that works directly on phones. This acoustic-based SLU system consists of only two blocks and does not require the presence of ASR module. The first block is a universal phone recognition system, and the second block is a Transformer-based language model for phones. We verify the effectiveness of the system on an intent classification dataset in Mandarin Chinese.