A perception‐based LSP distance measure for speech recognition

Edit Distance Comparison Confidence Measure for Speech Recognition

Formant weighted cepstral feature for LSP-based speech recognition

In this paper, we propose a formant weighted cepstral feature for LSP-based speech recognition system. The proposed weighting scheme is based on the well-known property of LSPs that the speech spectrum has a peak when adjacent LSFs come close. By applying this scheme to pseudo-cepstrum (PCEP) conversion process [1], we can obtain formant weighted or peak enhanced cepstral feature. Results of sp...

A Micropower Current-Mode Euclidean Distance Calculator for Pattern Recognition

In this paper a new synthesis for circuit design of Euclidean distance calculation is presented. The circuit is implemented based on a simple two-quadrant squarer/divider block. The circuit that employs floating gate MOS (FG-MOS) transistors operating in weak inversion region, features low circuit complexity, low power (<20uW), low supply voltage (0.5V), two quadrant input current, wide dyn...

Minimum Risk Distance Measure for Object Recognition

The optimal distance measure for a given discrimination task under the nearest neighbor framework has been shown to be the likelihood that a pair of measurements have different class labels [5]. For implementation and efficiency considerations, the optimal distance measure was approximated by combining more elementary distance measures defined on simple feature spaces. In this paper, we address...

A discriminative locally weighted distance measure for speaker independent template based speech recognition

In template based speech recognition, there is a need for a high-performant distance measure between speech frames. Some well known metrics include the Euclidean and the Mahalanobis distance. The recent tendency is to perform a local scaling of the distance metric, defining a set of classes and computing a set of weights for each of these classes. Discriminative training approaches have already...