Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Ac tsumugu 20170712

470 visualizaciones

Publicado el


Publicado en: Ingeniería
  • Sé el primero en comentar

  • Sé el primero en recomendar esto

Ac tsumugu 20170712

  1. 1. Afective computing and emotions in AI AKAOKA BADSSI Sara Empath Inc. 12/07/2017
  2. 2. Introduction AI and technology more and more vital in life and society Almost every domain field relies on it: medical, entertainment.. computers not only actors in technology but also in society How to make more sociable machines? Many researchers agree that emotions are part of the answer
  3. 3. Emotional intelligence In social interactions, emotions carry pieces of information: express one's intentions, interests in the conversation and state of mind understanding those emotions can improve the interaction between the parties Isen: emotions can have an infuence on the thinking and reasoning some tasks are more suitable for some emotions greeting happiness, comfort compassion => exploit and optimize the abilities of the interlocutor by infuencing their emotion Not only social context: humans need emotions for their survival and adaptation to society Ex: fear of dangerous situations makes people avoid the danger understanding the emotions, their origin and consequences = emotional intelligence Tis intelligence allows an individual to make beter decisions for their social and professional integration.
  4. 4. Afective computing Domain of human-machine interaction Goal: expand the human emotional intelligence to the machines overcome the emotional and social gap between human and computers create socially intelligent machines capable to respond approprietaly according to the situation and the interlocutor
  5. 5. Afective computing: Discrete approach Lots of theories based on both discrete and continuous approaches positive/negative emotions, primary/secondary... Discrete theory: Paul Ekman - 6 basic emotions: happiness, anger, fear, neutral, sadness and disgust - the rest of the emotions can be computed as a combination of those basic ones Strong points: universality of the emotion recognition a basis of a small number of emotions Weak points: more negative emotions than positive multiple expressions for one emotion
  6. 6. Afective computing: Continuous approach Russell's theory: All of the emotions can be described with only arousal and valence Strong point: only two dimensions, theoritically one could extract all of the emotions with this Weak point: how to measure those parameters? Which parameters correspond to arousal? And intensity?
  7. 7. Empath's challenges Goal: recognize emotions regardless of the language Strong points: ● A lot of researches and theories about affective computing, not much practice ● a lot of studies been done in speech processing, and we can communicate with machines Challenges: ● Affective computing mostly done on facial expression The universality that Ekman proved is for facial expression only ● The speech processing that we know is based on words, not emotions. We know which parts of the spectogram, of the vocal properties take into account for speech synthesis or recognition, but not emotions Challenge: Combine both speech and emotions
  8. 8. Empath's approach Pre-processing the data: - pitch - intensity - speech rate However, some more or less major obstacles come in the way: - how to extract those information accuratly and quickly (real-time) - choice of the model: Random Forest, NN, LSTM... - still lots of debates about the accuracy of these findings - individual characteristics (tone, pitch, natural intensity...) - context and culture Need of data: 4 emotions, 5 expressions each, 2 genders, 3 types of voices (child, adult, senior), 1 culture: 240000 samples needed one solution: adding prior information
  9. 9. What's next? “Soon enough it was discovered that it was difficult to find specific voice cues that could be used as reliable indicators of vocal expressions.Whereas listeners seem to be accurate in decoding emotions from voice cues, scientists have been unable to identify a set of cues that reliably discriminate among emotions.” (Petri Laukka – Vocal Expression of Emotion. Descrete-emotions and Dimensional Accounts – 2004) Is it a lost cause then? Not one right answer, but rather a combination of answers, provide accurate additional information. Emotions don't carry the entire message and information, they carry another type of information, different from the one carried in speech and words.
  10. 10. Thank you for your attention!