Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a novel approach to spoken language recognition using an ensemble of binary classifiers. In this framework,  begin by representing a speech utterance with a high-dimensional feature vector such as the phonotactic characteristics or the polynomial expansion of cepstral features. A binary classifier can be built based on such feature vectors.  adopt a distributed output coding strategy in ensemble classifier design, where  decompose a multiclass language recognition problem into many binary classification tasks, each of which addresses a language recognition subtask by using a component classifier. Then,  combine the results of the component classifiers to form an output code as a hypothesized solution to the overall language recognition problem. In this way,  effectively project high-dimensional feature vectors into a tractable low-dimensional space, yet maintaining language discriminative characteristics of the spoken utterances. By fusing the output codes from both phonotactic features and cepstral features,  achieve equal-error-rates of 1.38% and 3.20% for 30-s trials on the 2003 and 2005 NIST language recognition evaluation databases.
['Bin Ma', 'Haizhou Li', 'Rong Tong']
I am writing a research paper on a idea Spoken Language Recognition Using Ensemble Classifiers
[{"from": "human", "value": "I am writing a research paper on a idea Spoken Language Recognition Using Ensemble Classifiers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  study a novel approach to spoken language recognition using an ensemble of binary classifiers. In this framework,  begin by representing a speech utterance with a high-dimensional feature vector such as the phonotactic characteristics or the polynomial expansion of cepstral features. A binary classifier can be built based on such feature vectors.  adopt a distributed output coding strategy in ensemble classifier design, where  decompose a multiclass language recognition problem into many binary classification tasks, each of which addresses a language recognition subtask by using a component classifier. Then,  combine the results of the component classifiers to form an output code as a hypothesized solution to the overall language recognition problem. In this way,  effectively project high-dimensional feature vectors into a tractable low-dimensional space, yet maintaining language discriminative characteristics of the spoken utterances. By fusing the output codes from both phonotactic features and cepstral features,  achieve equal-error-rates of 1.38% and 3.20% for 30-s trials on the 2003 and 2005 NIST language recognition evaluation databases."}]