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In this paper we show how common training criteria like for example MPE or MMI can be extended to incorporate a margin term. In addition, a transducer-based training implementation is presented, which covers a large variety of discriminative training criteria for ASR, including the standard MMI, MPE, and MCE criteria, as well as the modifications to these criteria presented here. The modified criteria...
In this paper, we propose two methods of multiple time-resolution analysis of speech and their application to automatic speech recognition (ASR). Constant frame-rate multi-scale analysis is proposed based on a box of multi-scale features. Then a variable rate analysis is proposed based on the selection of the optimal temporal resolution on the fly by a properly trained non-linear classifier unit....
In this article, we studied the objective functions of MMI, MCE, and MPE/MWE for discriminative learning in sequential pattern recognition. We presented an approach that unifies the objective functions of MMI, MCE, and MPE/MWE in a common rational-function form of (25). The exact structure of the rational-function form for each discriminative criterion was derived and studied. While the rational-function...
In continuous speech recognition substitution, insertion and deletion errors usually not only vary in numbers but also have different degrees of impact on optimizing a set of acoustic models. To balance their contributions to the overall error, an enhanced minimum classification error (E-MCE) learning framework is developed. The basic idea is to partition acoustic model optimization into three subtasks,...
In this paper we compare three frameworks for discriminative training of continuous-density hidden Markov models (CD-HMMs). Specifically, we compare two popular frameworks, based on conditional maximum likelihood (CML) and minimum classification error (MCE), to a new framework based on margin maximization. Unlike CML and MCE, our formulation of large margin training explicitly penalizes incorrect...
In recent years, various discriminative learning techniques for HMMs have consistently yielded significant benefits in speech recognition. In this paper, we present a novel optimization technique using the minimum classification error (MCE) criterion to optimize the HMM parameters. Unlike maximum mutual information training where an extended Baum-Welch (EBW) algorithm exists to optimize its objective...
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