He Huang

Affiliations:
  • NVIDIA, Santa Clara, USA


According to our database1, He Huang authored at least 15 papers between 2023 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

2023
2024
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5
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Legend:

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In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning.
CoRR, 2024

META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR.
CoRR, 2024

Sortformer: Seamless Integration of Speaker Diarization and ASR by Bridging Timestamps and Tokens.
CoRR, 2024

Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR.
CoRR, 2024

NEST: Self-supervised Fast Conformer as All-purpose Seasoning to Speech Processing Tasks.
CoRR, 2024

BESTOW: Efficient and Streamable Speech Language Model with the Best of Two Worlds in GPT and T5.
CoRR, 2024

Less is More: Accurate Speech Recognition & Translation without Web-Scale Data.
CoRR, 2024

DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment.
CoRR, 2024

SALM: Speech-Augmented Language Model with in-Context Learning for Speech Recognition and Translation.
Proceedings of the IEEE International Conference on Acoustics, 2024

2023
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System.
CoRR, 2023

Property-Aware Multi-Speaker Data Simulation: A Probabilistic Modelling Technique for Synthetic Data Generation.
CoRR, 2023

Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition.
CoRR, 2023

Leveraging Pretrained ASR Encoders for Effective and Efficient End-to-End Speech Intent Classification and Slot Filling.
Proceedings of the 24th Annual Conference of the International Speech Communication Association, 2023

Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.
Proceedings of the International Conference on Machine Learning, 2023

Fast Conformer With Linearly Scalable Attention For Efficient Speech Recognition.
Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop, 2023


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