Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a new approach for detecting tone errors in continuous Mandarin speech. In the training phase, tone variations are modeled with context-depended MSD-HMM which considers six contextual factors instead of two in traditional triphone HMM. In the evaluation phase, the goodness of tone pronunciation is measured by Kullback-Leibler divergence (KLD) beten the expected tone model and the most representative tone model. When the KLD beten the two models is larger than a threshold, the tone is detected as a pronunciation error. In the ROC curve,  get the equal error rate at 2.6%.
['Yan-Bin Zhang', 'Min Chu', 'Chao Huang', 'Mangui Liang']
I am writing a research paper on a idea Detecting tone errors in continuous Mandarin speech
[{"from": "human", "value": "I am writing a research paper on a idea Detecting tone errors in continuous Mandarin speech"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a new approach for detecting tone errors in continuous Mandarin speech. In the training phase, tone variations are modeled with context-depended MSD-HMM which considers six contextual factors instead of two in traditional triphone HMM. In the evaluation phase, the goodness of tone pronunciation is measured by Kullback-Leibler divergence (KLD) beten the expected tone model and the most representative tone model. When the KLD beten the two models is larger than a threshold, the tone is detected as a pronunciation error. In the ROC curve,  get the equal error rate at 2.6%."}]