We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

摘要手写汉字识别(Handwritten Chinese character recognition, HCCR)
是模式识别的一个重要研究领域, 最近几十年来得到了广泛的研究与关注 …

Moving from limited-domain natural language generation (NLG) to open domain is difficult
because the number of semantic input combinations grows exponentially with the number of …

Methods for adapting and controlling the characteristics of output speech are important
topics in speech synthesis. In this work, we investigated the performance of DNN-based text …

In recent years, end-to-end (E2E) based automatic speech recognition (ASR) systems have
achieved great success due to their simplicity and promising performance. Neural …

In recent years, recurrent neural network language models (RNNLMs) have become
increasingly popular for a range of applications including speech recognition. However, the …

More recently, Bidirectional Encoder Representations from Transformers (BERT) was
proposed and has achieved impressive success on many natural language processing …

The effective incorporation of cross-utterance information has the potential to improve
language models (LMs) for automatic speech recognition (ASR). To extract more powerful …

We propose two adaptation models for recurrent neural network language models
(RNNLMs) to capture topic effects and longdistance triggers for conversational automatic …

Automated dialogue systems represent a promising approach for health care promotion,
thanks to their ability to emulate the experience of face-to-face interactions between health …