ASR:2015-05-18

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Speech Processing

AM development

Environment

  • grid-15 often does not work
  • grid-14 often does not work

RNN AM

  • details at http://liuc.cslt.org/pages/rnnam.html
  • Test monophone on RNN using dark-knowledge --Chao Liu
  • run using wsj,MPE --Chao Liu
  • run bi-directon --Chao Liu
  • train RNN with dark knowledge transfer on AURORA4 --zhiyuan

Mic-Array

  • hold
  • Change the prediction from fbank to spectrum features
  • investigate alpha parameter in time domian and frquency domain
  • ALPHA>=0, using data generated by reverber toolkit
  • consider theta
  • compute EER with kaldi

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

  • deliver to mengyuan

Speaker ID

  • DNN-based sid --Yiye Lin

Ivector&Dvector based ASR

  • hold --Tian Lan
  • Cluster the speakers to speaker-classes, then using the distance or the posterior-probability as the metric
  • Direct using the dark-knowledge strategy to do the ivector training.
  • Ivector dimention is smaller, performance is better
  • Augument to hidden layer is better than input layer
  • train on wsj(testbase dev93+evl92)

Dark knowledge

  • Ensemble using 100h dataset to construct diffrernt structures -- Mengyuan
  • adaptation English and Chinglish
  • Try to improve the chinglish performance extremly
  • unsupervised training with wsj contributes to aurora4 model --Xiangyu Zeng
  • test large database with AMIDA
  • test hidden layer knowledge transfer--xuewei

bilingual recognition

  • hold

language vector

  • train DNN with language vector--xuewei

Text Processing

RNN LM

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

W2V based document classification

  • make a technical report about document classification using CNN --yiqiao
  • CNN adapt to resolve the low resource problem

Translation

  • Test the performance of the similar-pair method in bilingual recognition

Order representation

  • modify the objective function
  • sup-sampling method to solve the low frequence word
  • Sort out vectors and do the experiment on objective function convergence
  • test on classification task and prediction task

binary vector

  • Finish hamming metric binary vector.
  • Try to finish binary vector.
  • Do test report.

Stochastic ListNet

  • To finish writing first edition of emnlp 2015 long paper

relation classifier

  • Tune the best model.
  • Train on new wordembedding.
  • Do some analysis(length of context, track the pooling.)
  • Finish the draft.

plan to do

  • combine LDA with neural network