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Tone recognition for continuous accented Mandarin Chinese

机译:连续语音重读普通话的音调识别

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In this paper, the ability of human listeners to recognize tones from continuous Mandarin Chinese is evaluated and compared to the accuracy of automatic systems for tone classification and recognition. All tones used for experimentation were extracted from the RASC863 continuous Mandarin Chinese database. The human listeners are native speakers of Mandarin and the automatic methods consist of tone classification using neural networks and tone recognition using Hidden Markov Models. Features used for the automatic methods are a combination of spectral/temporal features, energy contours, and pitch contours. When very little context is used (i.e., vowel segments only) the human and machine performance is comparable. However, as the context interval is increased, the human performance is much better than the best machine performance obtained.
机译:在本文中,评估了听众从连续普通话中识别语音的能力,并将其与自动系统进行语音分类和识别的准确性进行了比较。用于实验的所有音调均从RASC863连续汉语数据库中提取。听众是讲普通话的母语人士,自动方法包括使用神经网络进行音色分类和使用隐马尔可夫模型进行音色识别。用于自动方法的特征是频谱/时间特征,能量轮廓和音高轮廓的组合。当使用很少的上下文时(即仅元音段),人和机器的性能是可比的。但是,随着上下文间隔的增加,人工性能要比获得的最佳机器性能好得多。

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