Cool. OK. So the s the  the next thing we had on the agenda was something about alignments ?
Oh. Yes , we have  I don't know , did you wanna talk about it , or  ? I can give a  I was just telling this to Jane and  and  W we  we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors  that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a  a pruning  problem and possibly a problem with needing constraints on word locations. And so we tried both of these st things. We tried saying  I don't know , I got this  whacky idea that  just from looking at the data , that when people talk  their words are usually chunked together. It 's not that they say one word and then there 's a bunch of words together. They 're  might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average. So , um  And then also , ca the pruning , of course , was too  too severe.
So that 's actually interesting. The pruning was the same value that we used for recognition. And we had lowered that  we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in
Actually it was better with  slightly better or about th