Highly Accurate Mandarin Tone Classification In The Absence of Pitch Information

@inproceedings{Ryant2014HighlyAM,
  title={Highly Accurate Mandarin Tone Classification In The Absence of Pitch Information},
  author={Neville Ryant and M. Slaney and M. Liberman and E. Shriberg and Jiahong Yuan},
  year={2014}
}
  • Neville Ryant, M. Slaney, +2 authors Jiahong Yuan
  • Published 2014
  • Computer Science
  • A deep neural network (DNN) classifier based only on 40 mel-frequency cepstral coefficients (MFCCs) achieved 29.99% frame error rate (FER) and 16.86% segment error rate (SER) in recognizing five tonal categories in Mandarin Chinese broadcast news. With the addition of subband autocorrelation change detection (SACD) pitch-class features [1], the classifier scored 27.58% FER and 15.56% SER. These results are substantially better than the best previously reported results on broadcast news tone… CONTINUE READING
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