以深層卷積神經網路對中文語調進行分類

Mandarin Tone Classification Using CNN/DNN

陳釋澈(Shih-Che Chen)

指導教授 ： 張智星

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2018年)

https://doi.org/10.6342/NTU201802657

摘要

在華語系統中，聲調扮演十分重要之角色，同樣的一個音節，只要聲調的不同，即會產生完全不同的意義。母語是否為中文，常常可藉由講出來字詞之聲調辨認。為此，本論文提出一個對語音聲調進行分類的方法：先將聲音訊號轉為頻譜，將頻譜視為圖片，輸入至現有之影像識別卷積神經網路架構中，訓練出聲調分類模型，比較現成之影像辨識模型對處理聲調分類的效果如何。最後以此建立出不需對音訊進行過多處理步驟，即可達到一定程度之聲調分類架構。此聲調分類架構可套用至華語教學系統之中，為語言教學之方式提供新的選擇。

關鍵字

聲調分類 ； 頻譜 ； 影像識別 ； 卷積神經網路 ； 華語

並列摘要

In Mandarin Chinese system, the tone plays an important role. Different tone patterns of the same syllable may result in different meanings. People whose native language aren’t Mandarin can be distinguished by their tone patterns. Therefore, we propose a method for tone classification. First, we convert the audio signal into the spectrogram. We treat the spectrogram as images, apply them as the image inputs for image recognition convolutional neural networks, and create tone classification models. We compare different image recognition models for tone classification. This approach can achieve good accuracy without too many processes on the audio signal. The tone classification architecture can be applied to Chinese teaching methods which will lead to educational success.

並列關鍵字

tone classification ； spectrogram ； image recognition ； convolutional neural network ； Mandarin Chinese

參考文獻

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