Ariane Lazaridès

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Recently, we described a two-step self-learning approach for grapheme-to-phoneme (G2P) conversion [1]. In the first step, grapheme and phoneme strings in the training data are aligned via an iterative Viterbi procedure that may insert graphemic and phonemic nulls where required. In the second step, a Trie structure encoding pronunciation rules is generated.(More)
In the last few years, the power and simplicity of classification trees as acoustic modeling tools have gained them much popularity. In [1], we studied ''tree units'', which cluster parameters at the HMM level. Building on this earlier work, we examine some new variants of Young et al's ''tree states'', which cluster parameters at the state level [2]. We(More)
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