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首页> 外文期刊>International journal of computational systems engineering >Acoustic model combinations for continuous speech recognition system
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Acoustic model combinations for continuous speech recognition system

机译：连续语音识别系统的声学模型组合

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摘要

System combination is a promising way to obtain a significant improvement in performance as compared to the conventional form of single system model. In the field of automatic speech recognition (ASR), various approaches have been studied focusing on different aspects of feature extraction and acoustic modelling. These approaches can be combined to utilise their complementary information and to cope with the limitations of individual technique. In this paper we have proposed a novel approach in which three acoustic models based on maximum likelihood, discriminative and margin-based estimation are combined using a technique called as confusion network combination. Further, each acoustic model is associated with a different type of feature extractor to derive observation vectors for training and testing. Experimental results show 2%-5% reduction in error rate for Hindi ASR.
机译：与传统形式的单系统模型相比，系统组合是获得性能显着改善的有前途的方法。在自动语音识别（ASR）领域，针对特征提取和声学建模的不同方面，研究了各种方法。可以将这些方法结合起来以利用其补充信息并应对单个技术的局限性。在本文中，我们提出了一种新颖的方法，其中使用称为混淆网络组合的技术将基于最大似然，判别式和基于余量的三个声学模型进行组合。此外，每个声学模型与不同类型的特征提取器相关联，以导出用于训练和测试的观察向量。实验结果表明，印地语ASR的错误率降低了2％-5％。

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