Improved End-to-End Speech Recognition Using Adaptive Per-Dimensional Learning Rate Methods
نویسندگان
چکیده
منابع مشابه
Improved End-to-End Speech Recognition Using Adaptive Per-Dimensional Learning Rate Methods
The introduction of deep neural networks (DNNs) leads to a significant improvement of the automatic speech recognition (ASR) performance. However, the whole ASR system remains sophisticated due to the dependent on the hidden Markov model (HMM). Recently, a new endto-end ASR framework, which utilizes recurrent neural networks (RNNs) to directly model context-independent targets with connectionis...
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Achieving high accuracy with end-to-end speech recognizers requires careful parameter initialization prior to training. Otherwise, the networks may fail to find a good local optimum. This is particularly true for low-latency online networks, such as unidirectional LSTMs. Currently, the best strategy to train such systems is to bootstrap the training from a tied-triphone system. However, this is...
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Regularization is important for end-to-end speech models, since the models are highly flexible and easy to overfit. Data augmentation and dropout has been important for improving end-to-end models in other domains. However, they are relatively under explored for end-to-end speech models. Therefore, we investigate the effectiveness of both methods for end-to-end trainable, deep speech recognitio...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2016
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.2016sll0001