基于提升器和CTC的声调识别
Tone Recognition Using Lifters and CTC
摘要：
在本文中，我们提出了一种新的方法来识别声调语言的连续语音中的声调。该方法的工作原理是将语音信号转换为倒谱图，使用卷积神经网络提取倒谱特征序列，并使用连接时间分类（CTC）网络预测潜在的音调序列。该方法的性能进行了评估，一个免费提供的汉语普通话语音语料库，AISHELL-1，并表现出优于现有的技术在文献中的音调错误率（TER）。
英文摘要：
In this paper, we present a new method for recognizing tones in continuous speech for tonal languages. The method works by converting the speech signal to a cepstrogram, extracting a sequence of cepstral features using a convolutional neural network, and predicting the underlying sequence of tones using a connectionist temporal classification (CTC) network. The performance of the proposed method is evaluated on a freely available Mandarin Chinese speech corpus, AISHELL-1, and is shown to outperform the existing techniques in the literature in terms of tone error rate (TER).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
