Word-Free Spoken Language Understanding for Mandarin-Chinese
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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.
Key facts. Cited by 1 paper 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
9 of 10 references.
References · 9
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- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Speech Model Pre-training for End-to-End Spoken Language Understandingcited by 193
- 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
- 2015THCHS-30 : A Free Chinese Speech Corpuscited by 43
- 2020End-to-End Neural Transformer Based Spoken Language Understandingcited by 31
- 2020Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languagescited by 8
- 2020Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communitiescited by 5
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Citing papers per year: 2022: 1.
1 paper.