Class-Discriminative Weighted Distortion Measure for VQ-based Speaker Identification

We consider the distortion measure in vector quantization based speaker identification system. The model of a speaker is a codebook generated from the set of feature vectors from the speakers voice sample. The matching is performed by evaluating the distortions between the unknown speech sample and the models in the speaker database. In this paper, we introduce a weighted distortion measure that takes into account the correlations between the known models in the database. Larger weights are assigned to vectors that have high discriminating power between the speakers and vice versa

A variant of the Hough Transform for the combined detection of corners, segments, and polylines

The Hough Transform (HT) is an effective and popular technique for detecting image features such as lines and curves. From its standard form, numerous variants have emerged with the objective, in many cases, of extending the kind of image features that could be detected. Particularly, corner and line segment detection using HT has been separately addressed by several approaches. To deal with the combined detection of both image features (corners and segments), this paper presents a new variant of the Hough Transform. The proposed method provides an accurate detection of segment endpoints, even if they do not correspond to intersection points between line segments. Segments are detected from their endpoints, producing not only a set of isolated segments but also a collection of polylines. This provides a direct representation of the polygonal contours of the image despite imperfections in the input data such as missing or noisy feature points. It is also shown how this proposal can be extended to detect predefined polygonal shapes. The paper describes in detail every stage of the proposed method and includes experimental results obtained from real images showing the benefits of the proposal in comparison with other approaches