Yoojin’s Status Report for 4/26/20

Trained 4 models, male/female positive/negative emotion classification models and male/female multi-emotion classification models multi-emotion classification models perform poorly, so will be using binary classification instead Combined text and tone analysis Moved nets to cloud (but still need to work on getting it hooked up with the web app) Web app hooked up to ec2 instance

Vinay’s Status Report for 4/26/20

This week I focused on completing integration and preparing for the final presentation. Last week I was struggling with how to divide the journal entries into sentences.  Google Speech Recognition works best on 5 second snippets of audio. Additionally, the library is not able to recognize sentence boundaries. My solution was to record the entire journal entry and split it into sentences based on audio silence.This approximates of sentence boundaries worked well  for speakers with Read more…

Vinay’s Status Report for 4/18/20

I’ve reached 60% overall accuracy for the 7 categories (6 emotions + neutral). I’ve started to work on integration. I’m able to read speech from the computer mic and analyze it using python’s SpeechRecognition library. Right now, the program listens to a user until it hears a second of silence, then it closes the stream. Additionally, the entire journal entry is recorded as one long sentence with no punctuation. I need to figure out how Read more…

Team Status Report for 4/11/20

This week our group continued to work on our individual modules. Patrick trained around 60000 images for his facial emotion recognition and will add a secondary SVM for detecting emotions that are harder to tell apart. Vinay finished his first iteration of training for textual sentiment analysis. Yoojin added video streaming to the web app and is working on a speech tone emotion network. Going forward, we hope to finalize our networks and integrate them Read more…

Patrick’s Status Report for 4/11/20

Completed: Trained with 60000 images Balanced out images of each emotion Confusion matrix Accuracy of around 60-70 percent 7000 images – got around 80-90 percent but could be overfit Tested on my own face – contempt, anger, disgust, fear are confused Todo: Make second SVM for classifying hard to differentiate emotions Continue to up accuracy Train with more images Cross-validation?