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Maël
2019-06-25 12:18:17 +02:00
pai f27fbe98e8 f5d77ca6bf
commit 149bbf559b
21 arquivos alterados com 132 adições e 17 exclusões
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00-Presentation/Reports/PoleEmploi_Short_with_WebPage.pdf
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01-Audio/audio_app.png → 01-Audio/Images/audio_app.png
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01-Audio/README.md
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# Speech Emotion Recognition
![image](audio_app.png)
![image](Images/audio_app.png)
The aim of this section is to explore speech emotion recognition techniques from an audio recording.
02-Text/README.md
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# Text-based Personality Traits Recognition
![image](text_app.png)
![image](/00-Presentation/Images/text_app.png)
In this section you will find all resources, models and Python scripts relative to text-based personality traits recognition.
@@ -47,7 +47,7 @@ Gensim : 3.4.0
## Pipeline
![image](/Presentation/Images/text_pipeline.png)
![image](/00-Presentation/Images/text_pipeline.png)
The text-based personality recognition pipeline has the following structure :
- Text data retrieving
@@ -74,4 +74,4 @@ Following the three blocks, we chose to stack 3 LSTM cells with 180 outputs each
We tried different baseline models in order to assess the performance of our final architecture. Here are the accuracies of the different models.
![image](/Presentation/Images/perf_text_final.png)
![image](/00-Presentation/Images/perf_text_final.png)
02-Text/text_app.png
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04-WebApp/library/__pycache__/speech_emotion_recognition.cpython-35.pyc
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04-WebApp/library/__pycache__/text_emotion_recognition.cpython-35.pyc
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04-WebApp/library/__pycache__/text_preprocessor.cpython-35.pyc
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04-WebApp/library/__pycache__/video_emotion_recognition.cpython-35.pyc
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04-WebApp/static/js/audio_emotions_dist_other.txt
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EMOTION,VALUE
Angry,9
Disgust,19
Fear,2
Happy,2
Neutral,43
Sad,22
Surprise,0
04-WebApp/static/js/db/audio_emotions.txt
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EMOTIONS
Neutral
<<<<<<< HEAD
Neutral
Neutral
Neutral
@@ -11,3 +12,17 @@ Disgust
Disgust
Sad
Neutral
=======
Disgust
Disgust
Disgust
Angry
Angry
Angry
Disgust
Disgust
Disgust
Angry
Angry
Angry
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04-WebApp/static/js/db/audio_emotions_dist.txt
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EMOTION,VALUE
<<<<<<< HEAD
Angry,0
Disgust,16
=======
Angry,46
Disgust,46
>>>>>>> f5d77ca6bf9ee142e8625d0da5f5140497ddd45d
Fear,0
Happy,0
Neutral,75
Sad,8
Neutral,7
Sad,0
Surprise,0
04-WebApp/static/js/db/audio_emotions_other.txt
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@@ -597,6 +597,7 @@ Neutral
Neutral
Neutral
Neutral
<<<<<<< HEAD
Angry
Angry
Angry
@@ -621,6 +622,8 @@ Angry
Disgust
Angry
Angry
=======
>>>>>>> f5d77ca6bf9ee142e8625d0da5f5140497ddd45d
Neutral
Neutral
Neutral
@@ -629,9 +632,19 @@ Neutral
Neutral
Neutral
Neutral
<<<<<<< HEAD
Disgust
Disgust
Sad
=======
Neutral
Neutral
Neutral
Neutral
Neutral
Neutral
Neutral
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Neutral
Neutral
Neutral
@@ -643,5 +656,21 @@ Neutral
Neutral
Disgust
Disgust
<<<<<<< HEAD
Sad
Neutral
=======
Neutral
Disgust
Disgust
Disgust
Angry
Angry
Angry
Disgust
Disgust
Disgust
Angry
Angry
Angry
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04-WebApp/static/js/db/hist_vid_glob.txt
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EMOTION,VALUE
<<<<<<< HEAD
Angry,18
Disgust,8
Fear,12
@@ -6,3 +7,12 @@ Happy,235
Sad,323
Surprise,44
Neutral,436
=======
Fear,6
Disgust,5
Angry,17
Sad,71
Neutral,208
Surprise,17
Happy,103
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04-WebApp/static/js/db/hist_vid_perso.txt
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EMOTION,VALUE
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Angry,0
Disgust,3
Fear,0
@@ -6,3 +7,12 @@ Happy,24
Sad,0
Surprise,0
Neutral,8
=======
Fear,2
Disgust,5
Angry,4
Sad,21
Neutral,32
Surprise,5
Happy,18
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04-WebApp/static/js/db/text.txt
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@@ -151,8 +151,8 @@ Agreeableness,Conscientiousness,Extraversion,Neuroticism,Openness
0.2909279465675354,0.3018627464771271,0.2157977819442749,0.18610794842243195,0.00530355516821146
0.1821022480726242,0.15624618530273438,0.1497754454612732,0.3081181347370148,0.20375804603099826
0.1821022480726242,0.15624618530273438,0.1497754454612732,0.3081181347370148,0.20375804603099826
0.2476610541343689,0.22067716717720032,0.231057733297348,0.01261020451784134,0.2879939079284668
0.2476610541343689,0.22067716717720032,0.23105773329734802,0.01261020451784134,0.2879939079284668
0.2476610541343689,0.22067716717720032,0.231057733297348,0.01261020451784134,0.2879939079284668
0.2447648197412491,0.2237109392881393,0.2280386835336685,0.010952294804155828,0.292533278465271
0.2447648197412491,0.2237109392881393,0.2280386835336685,0.010952294804155828,0.292533278465271
0.0835515558719635,0.03762182593345642,0.11377312988042833,0.4335751831531525,0.3314782679080963
@@ -209,9 +209,13 @@ Agreeableness,Conscientiousness,Extraversion,Neuroticism,Openness
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
0.2451882064342499,0.22297443449497226,0.22819821536540985,0.010884138755500315,0.29275497794151306
<<<<<<< HEAD
0.3102996647357941,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
0.3102996647357941,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
0.3102996647357941,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
0.3102996647357941,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
0.3102996647357941,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
0.31029966473579407,0.18111537396907806,0.296110063791275,0.04382285103201866,0.1686520278453827
=======
0.24524909257888794,0.2229149490594864,0.22827190160751343,0.010896085761487484,0.292667955160141
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04-WebApp/static/js/db/text_mean.txt
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Trait,Value
<<<<<<< HEAD
Extraversion,0.21900552004161808
Neuroticism,0.08546119306184766
Agreeableness,0.2293632908689755
Conscientiousness,0.19947181524346685
Openness,0.26669816076927993
=======
Conscientiousness,0.20010490425513677
Extraversion,0.21685689027416763
Agreeableness,0.22713707076712242
Openness,0.2696092820580678
Neuroticism,0.0862918326110354
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04-WebApp/static/js/db/text_perso.txt
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Trait,Value
<<<<<<< HEAD
Extraversion,0.296110063791275
Neuroticism,0.04382285103201866
Agreeableness,0.31029966473579407
Conscientiousness,0.18111537396907806
Openness,0.1686520278453827
=======
Conscientiousness,0.2229149490594864
Extraversion,0.22827190160751343
Agreeableness,0.24524909257888794
Openness,0.292667955160141
Neuroticism,0.010896085761487484
>>>>>>> f5d77ca6bf9ee142e8625d0da5f5140497ddd45d
04-WebApp/static/js/db/words_common.txt
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@@ -119,6 +119,7 @@ computer,14
computing,2
conception,4
concise,2
confident,1
confront,2
consensus,4
consider,16
@@ -195,7 +196,7 @@ e,8
earlier,4
early,2
economic,2
ecosystem,4
ecosystem,5
edge,2
effort,2
email,4
@@ -227,7 +228,7 @@ facial,4
far,2
fatality,2
favourable,4
feel,6
feel,7
field,10
finance,6
financial,16
@@ -294,7 +295,7 @@ hundred,4
hybrid,2
idea,4
illustrate,4
impact,4
impact,5
important,6
importantly,2
impress,4
@@ -366,7 +367,7 @@ lettrepe,6
level,2
leverage,2
life,2
like,10
like,11
lionel,4
list,2
london,10
@@ -468,6 +469,7 @@ pleasure,4
point,4
portfolio,4
position,10
positive,1
post,2
precise,4
preferred,2
@@ -501,7 +503,7 @@ rank,8
raphael,6
reach,2
read,4
ready,2
ready,3
real,4
realise,4
realize,2
@@ -560,7 +562,7 @@ sit,2
situation,2
six,4
sixth,2
skill,12
skill,13
societe,4
sound,2
space,2
@@ -649,7 +651,11 @@ welcome,2
well,8
willing,4
word,2
<<<<<<< HEAD
work,38
=======
work,33
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working,2
would,18
write,4
04-WebApp/static/js/db/words_perso.txt
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WORDS,FREQ
<<<<<<< HEAD
work,1
group,1
project,3
@@ -25,3 +26,14 @@ top,1
downloaded,1
apps,1
lately,1
=======
feel,1
like,1
confident,1
impact,1
ready,1
work,1
skill,1
positive,1
ecosystem,1
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README.md
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- And kept the best model
<p align="center">
<img src="/Presentation/Images/Accuracy_Speech.png" width="400" height="400" />
<img src="/00-Presentation/Images/Accuracy_Speech.png" width="400" height="400" />
</p>
### c. [Video Analysis](https://github.com/maelfabien/Multimodal-Emotion-Recognition/tree/master/Video)