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- W2890817076 abstract "This paper proposes a new model for accurately rescoring (reranking) $N$ -best speech recognition hypothesis lists. The model is based on state-of-the-art neural networks (NNs) and provides the minimum necessary functionality to perform $N$ -best rescoring, i.e. one-on-one hypothesis comparison on a given $N$ -best list in terms of word error rates (WERs). The model is composed of a long short-term memory (LSTM)-based encoder network followed by a fully-connected feedforward NN-based binary-class classifier network. Given the feature vector sequences of two hypotheses to be compared, this encoder-classifier (EC) model encodes these features and outputs binary-class probabilities that indicate which hypothesis has the lower WER. Then, depending on the output, the ranks of these hypotheses can be swapped. By repeating this one-on-one hypothesis comparison, a reranked $N$ -best list can be obtained. In $N$ -best rescoring experiments using a large scale speech corpus, the proposed EC model steadily outperforms an LSTM-based language model (LSTMLM), which is a strong and widely-used competitor. In addition, by incorporating the LSTMLM scores as an additional feature vector dimension, the $N$ -best rescoring performance of the EC model is further improved. The improved EC model achieves a 10% relative WER reduction from the LSTMLM baseline." @default.
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- W2890817076 date "2018-04-01" @default.
- W2890817076 modified "2023-09-30" @default.
- W2890817076 title "Rescoring N-Best Speech Recognition List Based on One-on-One Hypothesis Comparison Using Encoder-Classifier Model" @default.
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- W2890817076 doi "https://doi.org/10.1109/icassp.2018.8461405" @default.
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