In this paper, we propose a new score normalization method for text-independent speaker verification using GMM (Gaussian Mixture Model). In the proposed method, cohort model is designed as virtual speaker model based on the similarity of local acoustic information between the reference speaker and other customers. The similarity is determined using statistical distance between model components ...

A fuzzy approach to normalization methods for pattern verification is presented in this paper. For an input object and a claimed identity, a claimed pattern's score is calculated and compared with a given threshold to accept or reject the claimed pattern. Considering the pattern verification problem based on fuzzy set theory, the claimed pattern's score is viewed as a fuzzy membership function....

This paper attempts to make an quantitative evaluation of available normalization techniques of matching scores in multimodal biometric systems. Two new normalization techniques Four Segments Piecewise Linear (FSPL) and Linear Tanh Linear (LTL) have been proposed in this paper. FSPL normalization techniques divides the region of genuine and impostor scores into four segments and maps each segme...

With this chapter we aims at describing several basic aspects of matching score level fusion. Section 14.1 provides a description of basic characteristics of matching score fusion in the form of introduction. Section 14.2 shows a number of matching score fusion rules. Section 14.3 surveys several typical normalization procedures of raw matching scores. Section 14.4 gives an example of matching ...

The demand for brown sugar products often falls below the level of production, resulting in unsold goods when market surpasses production capacity. This paper addresses challenge faced by many businesses estimating yields. Another issue, apart from uncertainty, is presence a dataset with significant nominal range. study focuses on specific producing company Indonesia. To address estimation prob...

In this paper, we describe the DeepNNNER entry to The 2nd Workshop on Noisy User-generated Text (WNUT) Shared Task #2: Named Entity Recognition in Twitter. Our shared task submission adopts the bidirectional LSTM-CNN model of Chiu and Nichols (2016), as it has been shown to perform well on both newswire and Web texts. It uses word embeddings trained on large-scale Web text collections together ...

User generated content often contains non-standard words that hinder effective automatic text processing. In this paper, we present a system we developed to perform lexical normalization for English Twitter text. It first generates candidates based on past knowledge and a novel string similarity measurement and then selects a candidate using features learned from training data. The system has a...