Papers by Tara Sainath
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation (2024.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that incorporating utterances without majority-agreed labels into an additional class reduces the classification performance of the other emotion classes. |
| Approach: | They propose to combine utterances without majority-agreed labels into an additional class . they propose to quantify uncertainty in emotion classification using evidential deep learning . |
| Outcome: | The proposed method retains classification accuracy while effectively detects ambiguous emotion expressions. |
Self-supervised Representation Learning for Speech Processing (2022.naacl-tutorials)
Copied to clipboard
Hung-yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff
| Challenge: | Self-supervised representation learning (SSL) uses proxy supervised learning tasks to obtain training data from unlabeled corpora. |
| Approach: | They propose to survey the latest SSL techniques, tools, datasets, and performance achievement in speech processing to scale up current machine learning technologies. |
| Outcome: | The proposed tutorial is highly relevant to the special theme of ACL about language diversity. |
Massive End-to-end Speech Recognition Models with Time Reduction (2024.naacl-long)
Copied to clipboard
Weiran Wang, Rohit Prabhavalkar, Haozhe Shan, Zhong Meng, Dongseong Hwang, Qiujia Li, Khe Chai Sim, Bo Li, James Qin, Xingyu Cai, Adam Stooke, Chengjian Zheng, Yanzhang He, Tara Sainath, Pedro Moreno Mengibar
| Challenge: | Using the neural architecture of Google’s universal speech model, we reduce the frame rate and speed up training and inference. |
| Approach: | They propose to use the neural architecture of Google’s universal speech model with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. |
| Outcome: | The proposed methods work with both connectionist temporal classification (CTC) and RNN-Transducer (RNN-T) and over two domains. |