Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to address the commonly met issue of overfitting in speech recognition, this article investigates Multi-Task Learning, when the auxiliary task focuses on speaker classification. Overfitting occurs when the amount of training data is limited, leading to an over-sensible acoustic model. Multi-Task Learning is a method, among many other regularization methods, which decreases the overfitting impact by forcing the acoustic model to train jointly for multiple different, but related, tasks. ,  consider speaker classification as an auxiliary task in order to improve the generalization abilities of the acoustic model, by training the model to recognize the speaker, or find the closest one inside the training set.  investigate this Multi-Task Learning setup on the TIMIT database, while the acoustic modeling is performed using a Recurrent Neural Network with Long Short-Term Memory cells.
['Gueorgui Pironkov', 'Stéphane Dupont', 'Thierry Dutoit']
I am writing a research paper on a idea Speaker-aware long short-term memory multi-task learning for speech recognition
[{"from": "human", "value": "I am writing a research paper on a idea Speaker-aware long short-term memory multi-task learning for speech recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In order to address the commonly met issue of overfitting in speech recognition, this article investigates Multi-Task Learning, when the auxiliary task focuses on speaker classification. Overfitting occurs when the amount of training data is limited, leading to an over-sensible acoustic model. Multi-Task Learning is a method, among many other regularization methods, which decreases the overfitting impact by forcing the acoustic model to train jointly for multiple different, but related, tasks. ,  consider speaker classification as an auxiliary task in order to improve the generalization abilities of the acoustic model, by training the model to recognize the speaker, or find the closest one inside the training set.  investigate this Multi-Task Learning setup on the TIMIT database, while the acoustic modeling is performed using a Recurrent Neural Network with Long Short-Term Memory cells."}]