This paper presents a question answering (QA) system developed for spoken lecture processing. The questions are presented to the system in written form and the answers are returned from lecture videos. In contrast to the widely studied reading comprehension style QA - the machine understands a passage of text and answers the questions related to that passage - our task introduces the challenge of searching the answers on longer text where the text corresponds to the erroneous transcripts of the lecture videos. Our initial experiments show that searching answers on longer text degrades the performance of the QA system drastically. Therefore, we propose splitting the transcriptions of lecture videos into short passages and determining passage-question matching using question aware passage representations. The proposed approach lets us utilize competitive neural network-based reading comprehension models for our task and improves the performance of the developed QA system.