Hello everyone,<br><br> Finally I also got my Neo Freerunner on Friday and I spent some time playing with it:). Here is the status update of this week. I finally completed the code book design code using vector quantization. Now the testing phase of the code is going on. I recorded various samples of word like "hello" in a .wav file and then using scilab I converted them into text files having arrays of numbers. The most challenging part in testing is the proper scaling and testing of each subroutine separately for fixed point notation. I also made an important change in fixed point by now using 8:8 notation than 16:16 as suggested by Erwin Lewin. However, I had to keep a track of all data types used in various subroutines for their ranges which was also interesting. While checking each subroutine separately,I found most of them giving correct results but some still needs to be modified for underflow and overflow problems.<br>
Besides this, some modification is being done in the noise rejection part since it can degrade the performance wildly. I will use zero crossing rate and short term energy algorithm for end point detection. My model will also use left to right HMM. But for properly training HMM models one needs more than one training sequence. It means that in speaker dependent recognition, for training any word, one needs to utter the same word two or three times so that proper modeling of HMM parameters take place. When more than one training sequence is used for training, baum welch or K-means segmental method gives better modeling of HMM parameters.<br>
<br>Next To Do:<br>1)Porting the whole code on openmoko platform:<br>2)testing with real adc channel of Freerunner<br>3)Proper testing of noise handling and recognition on freerunner<br><br clear="all"><br>-- <br>Saurabh Gupta<br>
Electronics and Communication Engg.<br>NSIT,New Delhi, India<br>I blog here: <a href="http://saurabh1403.wordpress.com">http://saurabh1403.wordpress.com</a><br><br>