Discriminative language models (DLMs) have been widely used for reranking competing hypotheses produced by an Automatic Speech Recognition (ASR) system. While existing DLMs suffer from limited generalization power, we propose a novel DLM based on a discriminatively trained Restricted Boltzmann Machine (RBM). The hidden layer of the RBM improves generalization and allows for employing additional prior knowledge, including pre-trained parameters and entity-related prior. Our approach outperforms the single-layer-perceptron (SLP) reranking model, and fusing our approach with SLP achieves up to 1.3% absolute Word Error Rate (WER) reduction and a relative 180% improvement in terms of WER reduction over the SLP reranker. In particular, it shows that proposed prior informed RBM reranker achieves largest ASR error reduction (3.1% absolute WER) on content words.
CITATION STYLE
Ma, Y., Cambria, E., & Bigot, B. (2018). ASR hypothesis reranking using prior-informed restricted boltzmann machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10761 LNCS, pp. 503–514). Springer Verlag. https://doi.org/10.1007/978-3-319-77113-7_39
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