Tone and pitch accent classification using auditory attention cues

A detailed description of tone and intonation is beneficial for many spoken language processing applications. In traditional methods for tone and pitch accent modeling, prosodic features, such as pitch, energy and duration, have been used. Here, a novel system that uses auditory attention cues is proposed for tone and fine grained pitch accent classification. The auditory attention cues are biologically inspired and hence extracted by mimicking the processing stages in the human auditory system. When tested on the Boston University Radio News Corpus, the proposed method achieves 64.6% pitch accent and 89.7% boundary tone classification accuracy. In addition, it is demonstrated that the model also successfully recognizes lexical tones in Mandarin with 79.0% accuracy when tested on a continuous Mandarin Chinese speech database. The results compare very well to the reported human performance on these tasks.