Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a unified i-vector framework for language identification (LID) based on deep bottleneck networks (DBN) trained for automatic speech recognition (ASR). The framework covers both front-end feature extraction and back-end modeling stages.The output from different layers of a DBN are exploited to improve the effectiveness of the i-vector representation through incorporating a mixture of acoustic and phonetic information. Furthermore, a universal model is derived from the DBN with a LID corpus. This is a somewhat inverse process to the GMM-UBM method, in which the GMM of each language is mapped from a GMM-UBM. Evaluations on specific dialect recognition tasks show that the DBN based i-vector can achieve significant and consistent performance gains over conventional GMM-UBM and DNN based i-vector methods. The generalization capability of this framework is also evaluated using DBNs trained on Mandarin and English corpuses.#R##N#Index Terms: Language Identification, Deep Neural Network, Deep Bottleneck Feature, i-vector representation
['Yan Song', 'Xinhai Hong', 'Bing Jiang', 'Ruilian Cui', 'Ian Vince McLoughlin', 'Li-Rong Dai']
I am writing a research paper on a idea Deep Bottleneck Network based I-Vector Representation for Language Identification
[{"from": "human", "value": "I am writing a research paper on a idea Deep Bottleneck Network based I-Vector Representation for Language Identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a unified i-vector framework for language identification (LID) based on deep bottleneck networks (DBN) trained for automatic speech recognition (ASR). The framework covers both front-end feature extraction and back-end modeling stages.The output from different layers of a DBN are exploited to improve the effectiveness of the i-vector representation through incorporating a mixture of acoustic and phonetic information. Furthermore, a universal model is derived from the DBN with a LID corpus. This is a somewhat inverse process to the GMM-UBM method, in which the GMM of each language is mapped from a GMM-UBM. Evaluations on specific dialect recognition tasks show that the DBN based i-vector can achieve significant and consistent performance gains over conventional GMM-UBM and DNN based i-vector methods. The generalization capability of this framework is also evaluated using DBNs trained on Mandarin and English corpuses.#R##N#Index Terms: Language Identification, Deep Neural Network, Deep Bottleneck Feature, i-vector representation"}]