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Constructing deep neural network (DNN) acoustic models from limited training data is an important issue for the development of automatic speech recognition (ASR) applications that will be used in various application-specific acoustic environments. To this end, domain adaptation techniques that train a domain-matched model without overfitting by lever-aging pre-constructed source models are widely...
This paper describes a method of cross-domain object categorization, using the concept of domain adaptation. Here, a classifier is trained using samples from the source/auxiliary domain and performance is observed on a set of test samples taken from a different domain, termed as the target domain. To overcome the difference between the two domains, we aim to find a sequence of optimally weighted sub-spaces,...
An important factor of a corpus is its domain, usually the quality of a SMT system trained on an in-domain corpus increases by adding out-of-domain sentences to its training corpus. In this paper we have shown out-of-domain corpora may also contains sentences which are proper for improving the quality of in-domain corpus. These sentences have words and phrases that occur in indomain corpora so, their...
In recent years, the transfer learning framework has gained increasing interest in the machine learning community. Fundamentally, this framework aims to train a new target system using existing data or knowledge from one or more previous source systems. By extending the theory of standard machine learning techniques, this framework allows us to solve many challenging problems directly and intuitively...
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