Interactive Spoken Content Retrieval with Deep Reinforcement Learning

• Main Authors: Yen-Chen Wu, 吳彥諶
• Other Authors: 李琳山
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/37478361307949330114

碩士 === 國立臺灣大學 === 電信工程學研究所 === 104 === Interactive retrieval is important for spoken content. The reason is because when looking for text documents, one can easily scan through and select on a search engine result page, whereas similar privileges don not exist when searching for spoken content. Besides, it is hard for the users to find the desired spoken content when the search results are noisy, which usually happens due to the imperfect speech recognition components in spoken content retrieval. A way to counter the difficulties of spoken content retrieval is human-machine interaction that machine takes different actions to request additional information from the user to obtain better retrieval results. The most suitable actions depend on the situations, so in previous works, some hand-crafted states estimated from the current search results are used to determine the actions, but the hand-crafted states are not necessary the best indicator for choosing actions. In this paper, we applied the Deep-Q- Learning method in interactive retrieval of spoken content. Deep-Q- Learning sidesteps the estimation of the hand-crafted states and can directly determine the action based on retrieval results without any human knowledge. It reached discernible improvements compared with the hand-crafted states.