Mandarin tone classification without pitch tracking

A deep neural network (DNN) based classifier achieved 27.38% frame error rate (FER) and 15.62% segment error rate (SER) in recognizing five tonal categories in Mandarin Chinese broadcast news, based on 40 mel-frequency cepstral coefficients (MFCCs). The same architecture scored substantially lower when trained and tested with F₀ and amplitude parameters alone: 40.05% FER and 22.66% SER. These results are substantially better than the best previously-reported results on broadcast-news tone classification [1] and are also better than a human listener achieved in categorizing test stimuli created by amplitude- and frequency-modulating complex tones to match the extracted F₀ and amplitude parameters.