Speech Recognition research has been ongoing for more than 80 years. Various attempts have been made to develop and improve speech recognition process around the world. Research on ASR by machine has attracted much attention over the last few decades. Bengali is largely spoken all over the world. There are lots of scopes yet to explore in the research regarding offline automatic Bangla speech recognition system. In our work, a moderate size speech corpus and a HMM based speech recognizer have been built to analyze the error pattern. Audio recordings have been collected from different persons in both quiet and noisy area. Live test has been carried out also to check the performance of the model individually. The percentage of the error and the percentage of correction with the created models are presented in this paper along with the results obtained during the live test. Finally, the results are analyzed to get the error pattern needed for future development.

In this paper, the bit error rate (BER) performance of SFBC-OFDM systems for frequency selective fading channels is observed for various antenna orientations and modulation schemes. The objective is to find out a suitable configuration with minimum number of receiving antenna that requires minimum signal power level at the receiver to provide reliable voice and video communication. We have considered both M-ary phase shift keying (MPSK) and M-ary quadrature amplitude modulation (MQAM) in the performance analysis considering both perfect and imperfect channel state information (CSI). The authors have expressed the BER under imperfect channel estimation condition as a function of BER under perfect channel condition in this paper. The finding shows, for a BTS with 4 transmitting antenna and MS with 2 receiving antenna BPSK performs better for both perfect and imperfect CSI. Maximum permissible channel estimation error increases with the usage of more receiving antenna at the expense of increased cost.

In this work, a 5 state left to right HMM-based Bangla Isolated word speech recognizer has been developed. To train and test the recognizer, a small corpus of various sampling frequencies have been developed in noisy as well as the noiseless environment. The number of filter banks is varied during the feature extraction phase for both MFCC and PLP. The effects of 2nd and 3rd differential coefficients have also been observed. Experimental results exhibit that MFCC based feature extraction technique is better in CLASSROOM environment on the contrary PLP based technique performs better not only in a noiseless environment but also in when AC or FAN noise is present. We have also noticed that higher sampling frequency and higher filter order don’t always help to improve the performance.