15 citations · 43 across the 9 of their papers we have counts for
9 papers
Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity
Mutian He, Philip N. Garner
Architectures such as Linformer and Mamba have recently emerged as competitive linear time replacements for transformers. However, corresponding large pretrained models are often u…
Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding
Mutian He, Philip N. Garner
Recently, large pretrained language models have demonstrated strong language understanding capabilities. This is particularly reflected in their zero-shot and in-context learning a…
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation
Mutian He, Philip N. Garner
End-to-end spoken language understanding (SLU) remains elusive even with current large pretrained language models on text and speech, especially in multilingual cases. Machine tran…
Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
Mutian He, Tianqing Fang, Weiqi Wang +1
Conceptualization, or viewing entities and situations as instances of abstract concepts in mind and making inferences based on that, is a vital component in human intelligence for…
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge
Mutian He, Jingzhou Yang, Lei He +1
End-to-end TTS requires a large amount of speech/text paired data to cover all necessary knowledge, particularly how to pronounce different words in diverse contexts, so that a neu…
Multilingual Byte2Speech Models for Scalable Low-resource Speech Synthesis
Mutian He, Jingzhou Yang, Lei He +1
To scale neural speech synthesis to various real-world languages, we present a multilingual end-to-end framework that maps byte inputs to spectrograms, thus allowing arbitrary inpu…