Implementation of a phoneme recognition system using zero-crossing and magnitude sum function

A number of techniques have been proposed in the literature for phoneme based speech recognition system. In this paper, a technique for automatic phoneme recognition using zero-crossings (ZC) and magnitude sum function (MSF) is proposed. The number of zero-crossings and magnitude sum function per frame are extracted and a minimum distance classifier is proposed to recognize the phonemes in each frame with these features. In order to increase the recognition accuracy of phonemes, a finite state machine is also proposed. The performance of the proposed phoneme recognition system is evaluated using TTS database and compared with the system using Linear Predictive Coefficients(LPC) feature inputs. Phoneme recognition accuracies of 70.93% and 55.25% are obtained for the system using LPC and the one using ZC along with MSF respectively. However, using the finite state machine proposed in this paper, 100% recognition accuracy is obtained for both the techniques. The computational costs required for recognizing various sentences using both of the feature extraction techniques are evaluated. It is observed that the proposed technique requires about 9.3 times lower computational cost than the one using LPC. The proposed phoneme recognition system is also implemented on an Altera Cyclone II FPGA using Nios II soft-core processor and custom instructions. The custom instructions for floating point arithmetic and Minimum distance classifier provide an acceleration factor of 41 and 1.87 respectively. The technique proposed here is also applicable for speech inputs from other database.