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In online handwritten math expression recognition, one-pass dynamic programming can produce high-quality symbol graphs in addition to best symbol sequence hypotheses, especially after discriminative training and trigram graph rescoring. Impact of symbol graphs on whole expression recognition, however, has not been referred to yet, since the interface of structure analysis module does not work well...
In this paper, we propose to use discriminative training (DT) for improving letter-to-sound (LTS) conversion performance. LTS is a critical component in both ASR and TTS for predicting the correct pronunciation of a word not included in the lexicon. For TTS applications, predicting the proper pronunciation of an out-of-vocabulary person/place name, especially a name with foreign origin can be challenging...
In the symbol recognition stage of online handwritten math expression recognition, the one-pass dynamic programming algorithm can produce high-quality symbol graphs in addition of the best recognized hypotheses. In this paper, we exploit the rich hypotheses embedded in a symbol graph to discriminatively train the exponential weights of different model likelihoods and the insertion penalty. The training...
Recently, we proposed a novel optimization algorithm called constrained line search (CLS) to train Gaussian mean vectors of HMMs in the MMI sense. In this paper, we extend and re-formulate it in a more general framework. The new CLS can optimize any discriminative objective functions including MMI, MCE, MPE/MWE etc. Also, closed-form solutions to update all Gaussian mixture parameters, including means,...
We propose to use minimum divergence, where acoustic similarity between HMMs is characterized by Kullback-Leibler divergence, for discriminative training. The MD objective function is defined as a posterior weighted divergence measured over the whole training set. Different from our earlier work, where KLD-based acoustic similarity is pre-computed for all initial models and stays invariant in the...
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