Build tree using bottleneck features

[vimalmanohar]

[danpovey]

Thanks! Can you share the objective function improvements from tree creation, in this vs. the baseline? It's from the log where the tree is built.

[vimalmanohar]

Baseline: /home/hhadian/kaldi/egs/iam/v1/exp_new/chain/tree_e2e/log/build_tree.log:VLOG[1] (build-tree[5.4.72-b6206]:BuildTree():build-tree.cc:196) After decision tree split, num-leaves = 500, like-impr = 15.6081 per frame over 974416 frames.
Bottleneck (1000 leaves): VLOG[1] (build-tree[5.4.129-90363]:BuildTree():build-tree.cc:196) After decision tree split, num-leaves = 1000, like-impr = 72.6673 per frame over 974416 frames.
Bottleneck (500 leaves) VLOG[1] (build-tree[5.4.129-90363]:BuildTree():build-tree.cc:196) After decision tree split, num-leaves = 500, like-impr = 65.6922 per frame over 974416 frames.

[danpovey]

yes, madcat is good I think.

On Sat, May 26, 2018 at 4:43 PM, Vimal Manohar [email protected] wrote:

@vimalmanohar commented on this pull request.

In egs/iam/v1/local/chain/run_cnn_e2eali_1d.sh https://github.com/kaldi-asr/kaldi/pull/2424#discussion_r191058172:

@@ -0,0 +1,259 @@ +#!/bin/bash

+# e2eali_1d is the same as e2eali_1b, but uses a tree trained using 200-dim

Ok. I'll fix this. Which setup should I try on? MADCAT?

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[danpovey]

@vimalmanohar, is anything happening with this? Is it still a candidate to be merged?

[jtrmal]

Closing and reopening, just to force status check on Travis

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