An ASR N-Best Transcript Neural Ranking Model for Spoken Content Retrieval

Spoken Content Retrieval (SCR) using ASR transcripts is in-creasingly important for multimedia content archives. How-ever, SCR is often impacted by ASR errors. In recent years novel neural ranking methods for information retrieval (IR) have achieved improved search effectiveness over established methods. In this study, we examine neural ranking methods in SCR. Specifically we introduce two new neural ranking methods designed for use with errorful ASR transcripts. In the first, we train a neural ranking model using both man-ual transcripts and ASR transcripts or manual transcripts and artificially created ASR transcripts. In the second, we use an ASR N-best extension of two existing neural ranking methods: Deep Relevance Matching Model (DRMM) and Position-Aware Convolutional Recurrent Matching (PACRR). We report two sets of SCR experiments which evaluate our neural ranking methods. In the first, we use ASR transcripts to retrieve documents from an archive of spoken instruction videos. In the second, we examine a known-item search on a collection of user-generated spoken videos. We find that training the neural ranking model on both ASR and man-ual transcripts improves Mean Reciprocal Rank (MRR). The N-best extension of PACRR is particularly effective in both experiments in comparison to a standard BM25 IR model.