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maglab-uconn / EARShot_N

Improved version of the EARShot model

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EARShot 1.1

Improved version of the EARShot model

Previous version: https://github.com/maglab-uconn/EARSHOT

Paper

Magnuson, J.S., You, H., Luthra, S., Li, M., Nam, H., Escabí, M., Brown, K., Allopenna, P.D., Theodore, R.M., Monto, N., & Rueckl, J.G. (2020). EARSHOT: A minimal neural network model of incremental human speech recognition. Cognitive Science, 44, e12823. http://dx.doi.org/10.1111/cogs.12823 -- PDF -- Supplmentary materials

Differences from Earlier version

  • Before RNN, Convoultion 1D as prenet can be added.
  • Some arg parser parameters are moved to 'Hyper_Paremeters.py' file.
  • Add pattern generator. Thus, the pattern parameters are changable.
  • Improved pattern generator and analyzer to enable large data learning and analysis in low memory environments

Requiremet

  • Tensorflow >=1.13 (It does not support TF 2.0 yet.)
  • Librosa >= 0.7.0
  • FFMPEG

Structure

Dataset

Wavfiles for acoustic pattern

  • This Data is generated by the Says of Mac here

  • Download diphone wav files for hidden analysis here

Hyper parameters

  • Pattern parameters

    • Word_List_File

      • List of words to use
      • Please see the example: 'ELP_groupData.csv'

    • Voice_Path

      • The path of dataset

    • Pattern_Path

      • The path of generated dataset

    • Pattern_Metadata_File_Name

      • The metadata file name.
      • Default is 'METADATA.PICKLE'

    • Pattern_Use_Bit

      • Parameter for when TensorFlow supports 16 bit operation
      • Currently is 32 fixed

    • Acoutsic_Mode

      • Determine which type of acoustic pattern use.
      • 'Spectrogram' or 'Mel'

    • Semantic_Mode

      • Determine which type of semantic pattern use.
      • 'SRV' or 'PGD'

    • Spectrogram

      • This parameters are used only 'Acoustic_Mode == 'Spectrogram'
      • Sample_Rate
      • Dimension
      • Frame_Length
      • Frame_Shift

    • Mel

      • This parameters are used only 'Acoustic_Mode == 'Mel'
      • Sample_Rate
      • Spectrogram_Dim
      • Mel_Dim
      • Frame_Length
      • Frame_Shift
      • Max_Abs
        • If a positive float, pattern is symetric '-Max_Abs to Max_Abs'
        • If 'None', non symmetric '0 to 1'. }),

    • SRV

      • This parameters are used only 'Semantic_Mode == 'SRV'
      • Size
        • Determine the size of pattern
      • Assign_Number
        • Determine How many units have 1. Other units have 0.

    • PGD

      • This parameters are used only 'Semantic_Mode == 'PGD'
      • Dict_File_Path
        • The path of pre generated dict pickle file.

  • Model parameters

    • Hidden_Type

      • Determine which type of RNN use.
      • 'LSTM', 'ZoneoutLSTM', 'GRU', 'SCRN', 'BPTT'

    • Hidden_Size

      • Determine RNN cell's size

    • Zoneout_Rate

      • This parameters are used only 'Hidden_Type == 'ZoneoutLSTM'
      • During learning, the zoneout rate is determined.

    • Prenet_Conv

      • Channels, Kernel sizes, and strides must be lists of the same size.
      • Use
        • If True, model uses prenet.
        • If false, the sub-parameters of 'Prenet_Conv' will be ignored.
      • Channels
        • Determine the number of filters in each convolution.
        • Ex: [512, 512, 512]
      • Kernel_Sizes
        • Determine the kernel size for each convolution.
        • Ex: [5, 5, 5],
      • Strides
        • Determine the stride size for each convolution.
        • Changing this value affects the number of time steps.
        • Ex: [1, 1, 1]
      • Use_Batch_Normalization
        • If true, model applies batch normalization after convolution.
      • Dropout_Rate
        • Determine a dropout rate during training.
        • *If 'None', there is no dropout. }),

    • Weight_Regularization

      • Use
        • If true, model uses weight regularization.
      • Except_Keywords
        • Variables containing the a keyword are excluded from weight regularization.
        • Mainly excludes RNN and Bias.
        • Ex: ['lstm', 'gru', 'scrn', 'rnn', 'bias']
      • Rate
        • Determines the strength of weight regularization applying.

    • Test_Timing

      • Determine the frequency of the inference during learning.
      • A positive integer is required.

    • Checkpoint_Timing

      • Determine the frequency of the checkpoint saving during learning
      • A positive integer is required.

    • Exclusion_Mode

      • Set pattern exclusion method. You can choose between P (pattern based), T (talker based), or M (mix based).
      • If set to P, 1/10 of each talker pattern will not be trained.
      • When set to T, all patterns of one talker are excluded from the learning. The talker can be set via the 'et' parameter.
      • When set to M, patterns are excluded as a mixture of the two methods.
      • When set to None, all patterns will be learned.

    • Test_Only_Identifier_List

      • Determines which talkers are always excluded regardless of the exclusion mode.
      • If nothing, 'None'
      • Ex: ['EO', 'JM'] or None

    • Max_Epoch_with_Exclusion

      • Apply 'Exclusion_Mode' and learn to set epoch.

    • Max_Epoch_without_Exclusion

      • From 'Max_Epoch_with_Exclusion' to the set epoch, 'Exclusion_Mode' is ignored and learned.
      • This is the parameter used to over-training all patterns after normal training.
      • This parameter must bigger than 'Max_Epoch_with_Exclusion'.

    • Learning_Rate

      • Determine the size of learning rate.
      • A positive float is required.

    • Batch_Size

      • Determine the batch size during learning.
      • When Out of memory occurs, decrease according to the environment(GPU memory).

    • Max_Queue

      • Determines the maximum size of queue saving the next training pattern

    • Extract_Path

      • Determine result and checkpoint save path

    • Result_Split

      • If True, the result data will be stored separately according to 'Batch_Size'.
      • It is recommended to separate if the environment is with a small memory or if there are a lot of learning patterns.
      • If False, save as one file.

Pattern generate

Command

python Run.Pattern_Generator.py

Model run

python EARShot.py [parameters]

Parameters

  • -se <int>

    • Set the model's start epoch. This parameter and the 'mf' parameter must be set when loading a previously learned model.
    • The default value is 0.

  • -et <talker>

    • Set which talker pattern is excluded from the learning.
    • Applies if 'em' parameter is T or M, otherwise this parameter is ignored.

  • -idx <int>

    • Attach an index tag to each result.
    • This value does not affect the performance of the model.

Result analysis

Command

python Result_Analysis.py [parameters]

Parameters

  • -d <path>

    • Results directory to run the analysis on.
    • This parameter is required.
    • Ex. ./Results/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0

  • -a <float>

    • Criterion in reaction time calculation of absolute method.
    • The default is 0.7.

  • -r <float>

    • Criterion in reaction time calculation of relative method.
    • The default is 0.05.

  • -tw <int>

    • Width criterion in reaction time calculation of time dependent method used in the paper.
    • The default is 10.

  • -th <float>

    • Height criterion in reaction time calculation of time dependent method used in the paper.
    • The default is 0.05.

Execution examples

python Result_Analysis.py -d ./Results/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0
python Result_Analysis.py -d ./Results/HM_LSTM.H_512.EM_M.ET_BRUCE.IDX_2 -tw 5 -th 0.1

Hidden analysis - Diphone based

Command

python Hidden_Analysis.py [parameters]

Parameters

  • -d <path>

    • Results directory to run the analysis on.
    • This parameter is required.
    • Ex. ./Results/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0

  • -e <int>

    • The epoch to run the analysis on.
    • This parameter is required.

  • -v <path>

    • diphone wav directory to be used for hidden analysis
    • This parameter is required.
    • Ex. ./Diphone_Wav

Execution examples

python Hidden_Analysis.py -d ./Results/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0 -e 600 -v ./Diphone_wav

Hidden analysis - Alignment based

Before analysis, alignment data must be generated.

Generating alignment data

python Alignment_Data_Extractor.py -d <path>
  • -d <path>
    • Alignment data directory
    • Ex. ./WAVS_ONLY_Padded_Alignment

Command

python Hidden_Analysis.Alignment.py [parameters]

Parameters

  • -d <path>

    • Results directory to run the analysis on.
    • This parameter is required.
    • Ex. ./Results/IDX_0/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0

  • -e <int>

    • The epoch to run the analysis on.
    • This parameter is required.

  • -v <path>

    • Alignment data directory to be used for hidden analysis
    • This parameter is required.
    • Ex. ./WAVS_ONLY_Padded_Alignment

RSA analysis

Command

python RSA_Analysis.py [parameters]

Parameters

  • -d <path>

    • Results directory to run the analysis on.
    • This parameter is required.
    • Ex. ./Results/IDX_0/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0

  • -e <int>

    • The epoch to run the analysis on.
    • To proceed with the RSA analysis, you must first perform a hidden analysis on the configured epoch.
    • This parameter is required.

  • -c <float>

    • Criterion of the PSI and FSI map to be used.

  • -pn <int>

    • Number of permutation tests
    • The default is 1000000.

Execution examples

python RSA_Analysis.py -d ./Results/IDX_0/HM_LSTM.H_512.EM_M.ET_AGNES.IDX_0 -e 2000 -c 0.0 -pn 10000

Generate figures by R script

Accuracy and cosine similarity flow

Method

  1. Use ./R_Script/Acc_and_CS_Flow(Fig.3).R

  2. Modify the parameters in line 116 - 123

  3. Run. The result will be in base_Dir.

Result example

PSI and FSI

Method

  1. Use ./R_Script/PSI_and_FSI(Fig.4).R

  2. Modify the parameters in line 36 - 41

  3. Run. The result will be in each talker's hidden analysis directory.

Result example

RSA permutation test

Method

  1. Use ./R_Script/RSA_Permutation_Test(Fig.5).R

  2. Modify the parameters in line 7 - 12

  3. Run. The result will be in each talker's hidden analysis directory.

Result example

Phoneme and feature flow

Method

  1. Use ./R_Script/Phoneme_and_Feature_Flow(Fig.6).R

  2. Modify the parameters in line 10 - 15

  3. Run. The result will be in each talker's hidden analysis directory.

Result example

Accuracy flow integration by talker

This is different 'Accuracy and cosine similarity flow'. This analysis checks the accuracy of the talker's data in all simulations.

Method

  1. Use ./R_Script/Acc_Flow_Integration_by_Talker(Fig.S2.1).R

  2. Modify the parameters in line 53 - 60

  3. Run. The result will be in base_Dir.

Result example

Phoneme and feature flow tile

Method

  1. Use ./R_Script/Phoneme_and_Feature_Flow_All_Tile(Fig.S2.2-3).R

  2. Modify the parameters in line 22 - 29

  3. Run. The result will be in each talker's hidden analysis directory.

Result example

Phoneme and feature flow compare

Method

  1. Use ./R_Script/Phoneme_and_Feature_Flow_Compare(Fig.S2.4).R

  2. Modify the parameters in line 10 - 17

  3. Run. The result will be in each talker's hidden analysis directory.

Result example

About

Improved version of the EARShot model

Languages

Language:Python 80.1%Language:R 19.9%