The frequency band between 5 010 MHz and 5 030 MHz allocated as C band has been used as a candidate in the global navigation satellite systems （GNSS） along with more and more naviga- tion services in L band. The pot...The frequency band between 5 010 MHz and 5 030 MHz allocated as C band has been used as a candidate in the global navigation satellite systems （GNSS） along with more and more naviga- tion services in L band. The potential benefits and technical requirements of C band for satellite navi- gation have been analyzed before. However the degradation of effective carrier-power-to-noise densi- ty ratio（ A （C/No ）eu） based on code tracking spectral sensitivity coefficient（ CT_SSC ） as a compati- bility assessment methodology for potential GNSS radio frequency compatibility in C-Band has not been discussed clearly. So the compatibility of the signals in the C band between BeiDou （BD） B1 C and GPS L1C, L1C/A, Galileo E1Os as the interoperability or classical signals in L band is analyzed. Simulation results reveal the interference degree between BD III B1C and GPS L1C/A, L1C, Galileo E1OS. The results can also reveal that the multiplexed binary offset carrier （MBOC） and binary phase shift keying （BPSK） modulation is not appropriate for C band.展开更多
In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance...In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.展开更多
Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the...Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the WUW-SR. The state of the art WUW-SR system is based on three different sets of features: Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding Coefficients (LPC), and Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC). In (front-end of Wake-Up-Word Speech Recognition System Design on FPGA) [1], we presented an experimental FPGA design and implementation of a novel architecture of a real-time spectrogram extraction processor that generates MFCC, LPC, and ENH_MFCC spectrograms simultaneously. In this paper, the details of converting the three sets of spectrograms 1) Mel-Frequency Cepstral Coefficients (MFCC), 2) Linear Predictive Coding Coefficients (LPC), and 3) Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC) to their equivalent features are presented. In the WUW- SR system, the recognizer’s frontend is located at the terminal which is typically connected over a data network to remote back-end recognition (e.g., server). The WUW-SR is shown in Figure 1. The three sets of speech features are extracted at the front-end. These extracted features are then compressed and transmitted to the server via a dedicated channel, where subsequently they are decoded.展开更多
基金Supported by the National High Technology Research and Development Program of China(863Program)(2011AA120502)
文摘The frequency band between 5 010 MHz and 5 030 MHz allocated as C band has been used as a candidate in the global navigation satellite systems （GNSS） along with more and more naviga- tion services in L band. The potential benefits and technical requirements of C band for satellite navi- gation have been analyzed before. However the degradation of effective carrier-power-to-noise densi- ty ratio（ A （C/No ）eu） based on code tracking spectral sensitivity coefficient（ CT_SSC ） as a compati- bility assessment methodology for potential GNSS radio frequency compatibility in C-Band has not been discussed clearly. So the compatibility of the signals in the C band between BeiDou （BD） B1 C and GPS L1C, L1C/A, Galileo E1Os as the interoperability or classical signals in L band is analyzed. Simulation results reveal the interference degree between BD III B1C and GPS L1C/A, L1C, Galileo E1OS. The results can also reveal that the multiplexed binary offset carrier （MBOC） and binary phase shift keying （BPSK） modulation is not appropriate for C band.
文摘In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.
文摘Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the WUW-SR. The state of the art WUW-SR system is based on three different sets of features: Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding Coefficients (LPC), and Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC). In (front-end of Wake-Up-Word Speech Recognition System Design on FPGA) [1], we presented an experimental FPGA design and implementation of a novel architecture of a real-time spectrogram extraction processor that generates MFCC, LPC, and ENH_MFCC spectrograms simultaneously. In this paper, the details of converting the three sets of spectrograms 1) Mel-Frequency Cepstral Coefficients (MFCC), 2) Linear Predictive Coding Coefficients (LPC), and 3) Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC) to their equivalent features are presented. In the WUW- SR system, the recognizer’s frontend is located at the terminal which is typically connected over a data network to remote back-end recognition (e.g., server). The WUW-SR is shown in Figure 1. The three sets of speech features are extracted at the front-end. These extracted features are then compressed and transmitted to the server via a dedicated channel, where subsequently they are decoded.
