Real-Time Speech Interruption Analysis: from Cloud to Client Deployment
Author:
Affiliation:
1. Vanderbilt University,Dept. of Computer Science
2. Microsoft Corporation
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10094559/10094560/10095639.pdf?arnumber=10095639
Reference21 articles.
1. Reducing transformer depth on demand with structured dropout;fan;8th International Conference on Learning Representations ICLR 2020,2020
2. EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation
3. LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT
4. Zero-Quant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers;yao,2022
5. Utilizing Self-Supervised Representations for MOS Prediction
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