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- W2641832364 abstract "Recent work on Acoustic Unit Discovery (AUD) has led to the development of a non-parametric Bayesian phone-loop model where the prior over the probability of the phone-like units is assumed to be sampled from a Dirichlet Process (DP). In this work, we propose to improve this model by incorporating a Hierarchical Pitman-Yor based bigram Language Model on top of the units' transitions. This new model makes use of the phonotactic context information but assumes a fixed number of units. To remedy this limitation we first train a DP phone-loop model to infer the number of units, then, the bigram phone-loop is initialized from the DP phone-loop and trained until convergence of its parameters. Results show an absolute improvement of 1–2%on the Normalized Mutual Information (NMI) metric. Furthermore, we show that, combined with Multilingual Bottleneck (MBN) features the model yields a same or higher NMI as an English phone recogniser trained on TIMIT." @default.
- W2641832364 created "2017-06-30" @default.
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- W2641832364 date "2017-03-01" @default.
- W2641832364 modified "2023-09-25" @default.
- W2641832364 title "Bayesian phonotactic Language Model for Acoustic Unit Discovery" @default.
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- W2641832364 doi "https://doi.org/10.1109/icassp.2017.7953258" @default.
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