Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In previous systems of speech emotion recognition, supervised learning are frequently employed to train classifiers on lots of labeled examples. Hover, the labeling of abundant data requires much time and many human efforts.  presents an enhanced co-training algorithm to utilize a large amount of unlabeled speech utterances for building a semi-supervised learning system. It uses two conditionally independent attribute views(i.e. temporal features and statistic features) of unlabeled examples to augment a much smaller set of labeled examples. Our experimental results demonstrate that compared with the method based on the supervised training, the proposed system makes 9.0% absolute improvement on female model and 7.4% on male model in terms of average accuracy. Moreover, the enhanced co-training algorithm achieves comparable performance to the co-training prototype, while it can reduce the classification noise which is produced by error labeling in the process of semi-supervised learning.
['Jia Liu', 'Chun Chen', 'Jiajun Bu', 'Mingyu You', 'Jianhua Tao']
I am writing a research paper on a idea Speech Emotion Recognition using an Enhanced Co-Training Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Speech Emotion Recognition using an Enhanced Co-Training Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In previous systems of speech emotion recognition, supervised learning are frequently employed to train classifiers on lots of labeled examples. Hover, the labeling of abundant data requires much time and many human efforts.  presents an enhanced co-training algorithm to utilize a large amount of unlabeled speech utterances for building a semi-supervised learning system. It uses two conditionally independent attribute views(i.e. temporal features and statistic features) of unlabeled examples to augment a much smaller set of labeled examples. Our experimental results demonstrate that compared with the method based on the supervised training, the proposed system makes 9.0% absolute improvement on female model and 7.4% on male model in terms of average accuracy. Moreover, the enhanced co-training algorithm achieves comparable performance to the co-training prototype, while it can reduce the classification noise which is produced by error labeling in the process of semi-supervised learning."}]