Person name spotting by combining acoustic matching and LDA topic models

In this article, we are interested in spoken term detection task, with a particular focus on Person Name (PN) spotting in automatic speech recognition (ASR) system outputs. We propose a two-step method that combines an acoustic matching based on a Phoneme Confusion Network (PCN) with a semantic rescoring based on the Latent Dirichlet Allocation (LDA) models. The first module allows to find, in the PCN, potential PN candidates in speech segments, while the second is in charge of ranking the competing PN, according to a LDA topic model. The proposed LDA-based approach outperforms significantly the baseline system based on a search in the ASR phoneme lattice, obtaining a F-measure score of 77.04% on PN detection.