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This paper proposes and compares four cross-lingual and bilingual automatic speech recognition techniques under the constraint that only the acoustic model (AM) of the native language is used at runtime. The first three techniques fall into the category of lexicon conversion where each phoneme sequence (PHS) in the foreign language (FL) lexicon is mapped into the native language (NL) phoneme sequence...
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 a novel, divergence-based similarity measure for spoken document retrieval (SDR). We derive a dynamic programming algorithm that measures Kullback-Leibler divergence between two HMMs first. The measure is further generalized to a graph matching algorithm, which is efficient for SDR application. The proposed approach compares the underlying acoustic models of keywords and a target database...
In this paper, we propose a novel constrained line search to optimize the MMEE objective function for training discriminative HMMs. In our method, the MMI estimation is cast as a constrained maximization problem, where Kullback-Leibler divergence between models before and after parameters adjustment is introduced as a constraint during optimization. Then, based on the idea of line search, we show...
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...
This paper proposes a new approach for measuring the target cost in unit selection, where the difference between the target and candidate units is estimated by the Kullback-Leibler divergence (KLD) between the context-dependent hidden Markov models (HMM). In order to model the left/right phonetic context, biphone models are generated by merging regular tri-phone HMMs sharing the same left/right phonetic...
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