Bayesian Neural Network Language Modeling for Speech Recognition

State-of-the-art neural network language models (NNLMs) represented by long short term memory recurrent neural networks (LSTM-RNNs) and Transformers are becoming highly complex. They are prone to overfitting and poor generalization when given limited training data. To this end, an overarching full B...

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Veröffentlicht in:	IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2022, Vol.30, p.1-16
Hauptverfasser:	Xue, Boyang, Hu, Shoukang, Xu, Junhao, Geng, Mengzhe, Liu, Xunying, Meng, Helen
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Audio data Audio equipment Bayes methods Bayesian analysis Bayesian learning Beamforming Computational modeling Computer architecture Gaussian process Machine learning Mathematical models model uncertainty neural architecture search neural language models Neural networks Parameters Recurrent neural networks Regularization Sentences Speech recognition Task analysis Training Transformers Uncertainty Voice recognition
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