ASR:2015-11-23

Speech Processing

AM development

Environment

• in disaster

RNN AM

• train monophone RNN --zhiyuan

• end to end MPE
• end to end using nnet3
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=446

Adapative learning rate method

• sequence training -Xiangyu

• write a technique report
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=458

Mic-Array

• hold
• compute EER with kaldi

Data selection unsupervised learning

• hold
• acoustic feature based submodular using Pingan dataset --zhiyong
• write code to speed up --zhiyong
• curriculum learning --zhiyong

RNN-DAE(Deep based Auto-Encode-RNN)

• hold
• RNN-DAE has worse performance than DNN-DAE because training dataset is small
• extract real room impulse to generate WSJ reverberation data, and then train RNN-DAE

Ivector&Dvector based ASR

• learning from ivector --Lantian

• CNN ivector learning
• DNN ivector learning

• binary ivector
• metric learning
• LDA-vector Transfer Learning
• write a technique report

language vector

• write a paper--zhiyuan

• hold

• language vector is added to multi hidden layers--zhiyuan

• write code done
• check code
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=480

• RNN language vector

• hold

multi-GPU

• multi-stream training --Sheng Su

• write a technique report

• kaldi-nnet3 --Xuewei

• 7*2048 8k 1400h tdnn training Xent done
• nnet3 mpe code is under investigation
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=472

• train 7*2048 tdnn using 4000h data --Mengyuan

multi-task

• test according to selt-information neural structure learning --mengyuan

• hold
• write code done
• no significant performance improvement observed

• speech rate learning --xiangyu

• hold
• no significant performance improvement observed
• http://192.168.0.51:5555/cgi-bin/cvss/cvss_request.pl?account=zxw&step=view_request&cvssid=483

test using extreme data

Text Processing

RNN LM

• character-lm rnn(hold)
• lstm+rnn

• check the lstm-rnnlm code about how to Initialize and update learning rate.(hold)

Neural Based Document Classification

• (hold)

RNN Rank Task

• Test.
• Paper: RNN Rank Net.

• (hold)
• Output rank information.

Graph RNN

• Entity path embeded to entity.

• (hold)

RNN Word Segment

• Set bound to word segment.

• (hold)

Seq to Seq(09-15)

• Review papers.
• Reproduce baseline. (08-03 <--> 08-17)

Order representation

• Nested Dropout

• semi-linear --> neural based auto-encoder.

• modify the objective function(hold)

Balance Representation

• Find error signal

Recommendation

• Reproduce baseline.

• LDA matrix dissovle.
• LDA (Text classification & Recommendation System) --> AAAI

RNN based QA

• Read Source Code.
• Attention based QA.
• Coding.

RNN Poem Process

• Seq based BP.

• (hold)

Text Group Intern Project

Buddhist Process

• (hold)

RNN Poem Process

• Done by Haichao yu & Chaoyuan zuo Mentor : Tianyi Luo.

RNN Document Vector

• (hold)

Image Baseline

• Demo Release.
• Paper Report.

• Read CNN Paper.

Text Intuitive Idea

Trace Learning

• (Hold)

Match RNN

• (Hold)

financial group

model research

• RNN

• online model, update everyday
• modify cost function and learning method
• add more feature

rule combination

• GA method to optimize the model

basic rule

• classical tenth model

multiple-factor

• add more factor
• use sparse model

display

• bug fixed

• buy rule fixed

data

• data api

• download the future data and factor data