THEY PICKED UP THE SOFTWARE AND THEY PLAY WITH THE AND NETS SO THAT MIGHT BE THAT BRNO WILL BE AT THE SOONEST AS UH A ULTIMATE EXPERT ON ON THE WHOLE THING OLIVIER BORNET AND NO THEY NE QUICKNET WAS DONE UH UH WELL QUICKNET ITSELF WAS DONE ACTUALLY LONG TIME AGO AT ICSI IS THAT SO GROUP BECAUSE WE COULDN'T WE COULDN'T DO SOMETHING I DON'T KNOW WHAT BUT HE IS HE IS NO GOOD IN QUICKNET RIGHT I S I GUESS YEAH UM OKAY SO SO RIGHT SO YOU JUST MISS THE BACK END WHICH IS EASY BECAUSE IT'S H. T. K. IT'S EVERYONE KNOWS HERE SO I HAVE THE GENERAL FEEL BUT UH MAYBE I CAN TAKE THE INTERCEPTOR FROM YOU AND RUN IT ONCE ON MY TO HAVE IT A AND YOU YOU JUST NEED FROM US TO HEAR W WHAT ARE THE OTHER P FRONT ENDS AND OTHER FEATURES TO TO CHOOSE OR SOMETHING YEAH BASICALLY SO THEN I CAN CAN SHARE WHAT I CA HAVE READ AND UH WELL THE WHOLE IDEA THEN IS THAT THEN YOU START PLAYING WITH IT RIGHT AND YOU YEAH AND OKAY SO HOW DO WE DO IT SHOU SHOU SHOULD I LIST IT OR MAYBE THAT MIGHT BE THE LET'S DO THE FEATURE EXTRACTION THAT WE HAVE ALREADY TAKEN THEN THE BACK END FOR THAT THEN YEAH BECAUSE SEE YOU FEED SOMETHING TO THE M. L. P. WHICH ARE NOT THE WAVES SO SO WELL AND THAT'S THAT'S WHAT I CALL IT AND THEN THE BACK END BUT OKAY WELL SO WHAT WHAT I HAVE HERE WHAT I'VE BEEN WORKING ON SINCE I WAS HERE WAS M. F. C. C. P. L. P. WHICH IS ALWAYS NEEDED FOR COMPRESSING PURPOSES THEN TRAPS WITH W VARIOUS LENGTHS AND ALSO UH YOU KNOW TRAPS NOW TRAPS I KNOW BUT THOUGH WITH VARIOUS WITH VAR VARIOUS LENGTHS WE WERE TRYING TO TO START THEM FROM FROM ON THE ULTIMATE LENGTH OF ONE ONE UH FRAME UP TO ONE SECOND OR EVEN MORE BUT IT WAS MM SO THIS ALSO W THEN WE APPLY THE D. C. T. ON THE TRAP ITSELF WHICH IS SOME SOMETHING LIKE THE GAUSSIANS APPLYING SOME FILT FILTERING BASICALLY THE TRAP SO THIS BUT THIS IS MAINLY FRANTA WHO WAS DOING THIS MM-HMM SO THIS IS RELATED TO TRAPS I WAS ALSO TRYING THE WARPING MEANS IF YOU HAVE A SECOND UM WHICH IS UH A HUNDRED AND ONE SAMPLES I WAS JUST KEEPING THE CENTRAL TEN TEN SAMPLES AND THEN I WAS THROWING AWAY SOME SOME OF THEM YOU KNOW LIKE WITH SAMPLING THIS THIS UH ONE SECOND OF THE TRAP TO SOMETHING WHICH I WHICH I UH PRESERVED ALL THE POINTS IN THE CENTRE AND JUST UH PICKED EVERY TENTH POINT AT THE BOUNDARIES WHICH MEANS THE TRAP UH HUNDRED AND ONE TRAP TRAJECTORY SHRANK TO SAY FIFTEEN POINTS WITH UH WITH STILL PRESERVING THE MAXIMUM RESOLUTION AT THE CENTRE AND THROWING AWAY MOST OF THE POINTS NEAR THE BOUNDARY SO THIS I WAS TRYING SO THESE I HAVE SOME RESULTS ON SO THESE ARE TRAPS THEN THE GAUSS TRAPS THEN TANDEM SYSTEM OKAY UH THE TANDEM YOU PLUS MINUS HALF BECAUSE THAT'S THAT'S JUST A P. L. P. S AND THEN THERE IS A TRAINING UH WITH M. L. P. THAT'S TAKING SOME CONTEXT OF THESE P. L. P. COEFFICIENTS SO IT'S LIKE FEEDING THE P. L. UH P. L. P. S OR M. F. C. S W WHATEVER TO M. L. P. AND TAKING SOME CONTEXT THIS IS THE TANDEM SO THIS SET UP WE ALSO HAVE HERE I DIDN'T EXPERIMENT WITH THIS BUT MAYBE YOU SOME MORE RESULTS OH IT'S HEMANT WHO HAS THE RESULTS ON TANDEM I GUESS NO YEAH YEAH RIGHT YEAH BUT UH I THINK THE UH I THINK THE MOST IMPORTANT RESULTS ARE ALREADY IN THE THESIS HE'S WRITING SO SO IS THERE SOME SOMEWHERE SO THIS IS TANDEM UH THEN UH THEN WHAT AND THEN THERE IS THE F. D. L. P. THEN WE CAN TALK MORE ABOUT WHICH IS WHICH I HAVE MORE EXPERIMENTS ON ALSO THIS WARPING AND COMPRESSIONS AND ALL THESE THINGS SO YEAH YEAH BUT I'M USING THAT FOR DIFFERENT DIFFERENT REASON RIGHT SO IT'S UH SPEECH CODING SO UH WHAT IS THE TASK IS USUAL JUST TAKE THE SPEECH OF COURSE SOMEHOW TO ENCODE IT INTO AS AS SMALL NUMBER OF BEAT AS POSSIBLE AND WITH A REASONABLE QUALITY OF COURSE AND AGAIN TO E UH DECODE IT AND I'M USING F. D. L. P. FOR THAT STILL PLAYING WITH UH MORE LESS ONE SECOND LONG SEGMENTS SO JUST DIVIDING THE SPEECH INTO ONE S ONE SECOND FRAMES DOING D. C. T. IT'S JUST THE STANDARD WOR OF WORK AND THEN DOING SOME COMPRESSION AND COMPUTATION OF YEAH YEAH YEAH SH OH YEAH BUT YOU ARE DOING THE SAME NO YEAH B YEAH YEAH YOU TAKE THE THE D. C. T. AND YOU JUST DIVIDE IT INTO SEVERAL SUB BANDS FREQUENCY BANDS WITH A DIFFERENT UH AND BY THE WAY YOU DON'T DO THE OVERLAP ON THE SUB BANDS NO OR YOU DO YEAH BUT YOU DON'T DO THE OVERLAP ON THE TIME DOMAIN YOU SAID SOMETHI YEAH OKAY SO THAT'S THE DIFFERENCE YEAH JUST ONE SECOND WITH WITHOUT OVERLAPPING BUT IN FREQUENCY DOMAIN THERE IS A OVERLAP BECAUSE YOU CAN CHOOSE WHICH FILTERS YOU WANT MORE LESS OR JUST TRAJECTORIES OF THOSE BANDS SO THERE IS OVERLAPPING I'M USING THOSE GAUSSIAN FILTERS OR CLASSICAL THOSE THOSE TRAPET TRAPEZOIDAL LIKE IN UH M. F. C. C. S RIGHT AND THEN PROCESSING EACH F YEAH P. L. P. S OKAY TR TRIANGULARS THAT ONE THEN I'M DOING S DOING UH INDEPENDENT PROCESSING OF EACH FREQUENCY BAND SO I'M COMPUTING THAT HILBERT ENVELOPE WHICH IS JUST THE ENVELOPE OF SOME TEMPORAL TRAJECTORY AND MORE OR LESS YOU CAN DIVIDE THIS INTO A HILBERT ENVELOPE HILB HILBERT CARRIER WE USE SOMETHING LIKE L. P. A LINEAR PREDICTION ENVELOPE AUTO REGRESSIVE MODEL AND EXCITATION SIGNAL YEAH SO THOSE ARE TWO PARTS OR ITEMS YOU NEED SOMEHOW TO ENCODE IF YOU WANT AGAIN TO GET ORIGINAL SPEECH OR REASONABLE QUALITY AND YEAH THERE IS SOME COMPRESSION FACTOR WHICH IS AGAIN WHEN YOU'RE COMPUTING THAT AUTO REGRESSIVE MODEL FROM F. D. L. P. YOU CAN COMPRESS THE SPECTRUM SOMEHOW IN IN SUCH A WAY THAT YOU RATHER UM PRESERVE THOSE EVEN ALSO THOSE DIPS NOT ONLY PEAKS IN THE SPECTRUM AND IT SOUNDS BETTER FOR SOME KIND OF SPEECH SIGNALS YEAH YEAH YEAH KNOWS MORE THAN THAN THAN I DO S SO THAT'S GOOD YEAH MM-HMM AND THERE OTHER OTHER THINGS WHICH I'M DOING BUT IT'S NOT RELATED TO SPEECH RECOGNITION SO MUCH OR BUT EVEN PE PETR FOUSEK IS NOT HERE YOU PROBABLY KNOW A LOT ABOUT IT BY NOW WELL WHAT RIGHT RIGHT I WHAT I I D YEAH WELL THE BEGINNING OF THE SURE SURE I KNOW I MEAN IT'S MORE OR LESS THE SAME APPROACH RIGHT THERE IS M UH JUST MORE RIGHT RIGHT RIGHT YEAH YEAH WITH WHAT I M NO NO