Automatic Speaker Recognition using LPCC and MFCC

A person's voice contains various parameters that convey information such as emotion, gender, attitude, health and identity. This report talks about speaker recognition which deals with the subject of identifying a person based on their unique voiceprint present in their speech data. Pre-processing of the speech signal is performed before voice feature extraction. This process ensures the voice feature extraction contains accurate information that conveys the identity of the speaker. Voice feature extraction methods such as Linear Predictive Coding (LPC), Linear Predictive Cepstral Coefficients (LPCC) and Mel-Frequency Cepstral Coefficients (MFCC) are analysed and evaluated for their suitability for use in speaker recognition tasks. A new method which combined LPCC and MFCC (LPCC+MFCC) using fusion output was proposed and evaluated together with the different voice feature extraction methods. The speaker model for all the methods was computed using Vector Quantization- Linde, Buzo and Gray (VQ-LBG) method. Individual modelling and comparison for LPCC and MFCC is used for the LPCC+MFCC method. The similarity scores for both methods are then combined for identification decision. The results show that this method is better or at least comparable to the traditional methods such as LPCC and MFCC. DOI: 10.17762/ijritcc2321-8169.16047

Conversion of NNLM to Back-off language model in ASR

In daily life, automatic speech recognition is one of the aspect which is widely used for security system. To convert speech into text using neural network, Language model is one of the block on which efficiency of speech recognition depends. In this paper we developed an algorithm to convert Neural Network Language model (NNLM) to Back-off language model for more efficient decoding. For large vocabulary system this conversion gives more efficient result. Efficiency of language model depends on perplexity and Word Error Rate (WER

Review of Reconfigurable Antennas for LTE, WiMAX and WLAN Application

To satisfy the requirement of advance wireless system various frequency and pattern reconfigurable antennas are designed. Monopole and PIFA antennas with reconfigurability are preferred for various handheld devices. The objective of this paper was to present a review of reconfigurable monopole and PIFA antennas used for LTE, WiMAX and WLAN frequency ranges. Various optimization techniques used for reconfigurable antenna are also reviewed

Single-Carrier Frequency-Domain Equalizer with Multi-Antenna Transmit Diversity

ABSTRACT: Single-carrier (SC) block transmission with cyclic prefix (CP) is a method with several advantages that has been incorporated into standards. This paper has analyzed the performance of multi-antenna SC-FDE under Alamouti signaling and cyclic-delay diversity (CDD). Our analysis shows that the characteristic of diversity it is depends on data block length and data transmission rate as well as on the channel memory and antenna configuration. At higher rates their diversity diminishes and full diversity is available to both CDD and Alamouti signalling below a certain rate threshold. From our investigation we say that at high rates Alamouti signalling provides twice the diversity of SISO SC-FDE, while the diversity of the SISO SC-FDE under the CDD diversity degenerates