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We examine the task of spoken term detection in Chinese spontaneous speech with a lattice-based approach. We first compare lattices generated with different units: word, character, tonal and toneless syllables, and also lattices converted from one unit to another unit. Then we combine lattices from multiple systems into a single lattice. By fully exploiting the redundant information in the combined...
We address the problem of how to represent or approximate speech lattices to be indexed with existing text indexers. We present a method named time-anchored lattice expansion (TALE), which can be implemented by a standard text indexer (STI). On a 170- hour lecture set, we compare TALE with other lattice indexing methods: confusion networks, position-specific posterior lattices (PSPL), and time-based...
We examine the task of spoken term detection in Chinese spontaneous speech with a lattice-based approach. We compare lattices generated with different units: word, character, tonal syllable and toneless syllable, and also look into methods of converting lattices from one unit to another one. We find the best system is with toneless-syllable lattices converted from word lattices. Further improvement...
Enterprise-scale search engines are generally designed for linear text. Linear text is suboptimal for audio search, where accuracy can be significantly improved if the search includes alternate recognition candidates, commonly represented as word lattices. We propose two methods to enable text indexers to approximately index lattices with little or no code change: "TMI" (Time-based Merging...
We propose a probabilistic model for estimating word confidence by fusing predictor features. Starting from the maximum entropy (ME) method, we first prove that ME model is equivalent to the best model with certain form to the minimum expected cross entropy (MECE) criterion. Under the MECE criterion, We extend the form of ME model by introducing a hidden state. We call the new model hidden-state maximum...
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