Search Results for author: Che-Wei Huang

Found 5 papers, 0 papers with code

Incremental user embedding modeling for personalized text classification

no code implementations13 Feb 2022 Ruixue Lian, Che-Wei Huang, Yuqing Tang, Qilong Gu, Chengyuan Ma, Chenlei Guo

Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education.

Management Multi-class Classification +3

Wav2vec-C: A Self-supervised Model for Speech Representation Learning

no code implementations9 Mar 2021 Samik Sadhu, Di He, Che-Wei Huang, Sri Harish Mallidi, Minhua Wu, Ariya Rastrow, Andreas Stolcke, Jasha Droppo, Roland Maas

However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2. 0 network from the quantized representations in a way similar to a VQ-VAE model.

Quantization Representation Learning +1

Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition

no code implementations27 Jul 2020 Jinxi Guo, Gautam Tiwari, Jasha Droppo, Maarten Van Segbroeck, Che-Wei Huang, Andreas Stolcke, Roland Maas

Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists.

speech-recognition Speech Recognition

Normalization Before Shaking Toward Learning Symmetrically Distributed Representation Without Margin in Speech Emotion Recognition

no code implementations2 Aug 2018 Che-Wei Huang, Shrikanth. S. Narayanan

Regularization is crucial to the success of many practical deep learning models, in particular in a more often than not scenario where there are only a few to a moderate number of accessible training samples.

Data Augmentation General Classification +1

Characterizing Types of Convolution in Deep Convolutional Recurrent Neural Networks for Robust Speech Emotion Recognition

no code implementations7 Jun 2017 Che-Wei Huang, Shrikanth. S. Narayanan

Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech.

Event Detection Speech Emotion Recognition +2

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