<p>As I mentioned in the comments, the tool we're using is <a href="https://github.com/tyiannak/pyAudioAnalysis" rel="nofollow noreferrer">pyAudioAnalysis</a>.  The tool's author, Theodoros Giannakopoulos, also wrote an earlier paper "<a href="https://www.researchgate.net/publication/220734016_A_dimensional_approach_to_emotion_recognition_of_speech_from_movies" rel="nofollow noreferrer">A dimensional approach to emotion recognition of speech from movies</a>"</p>

<p>This "Emotion Wheel" from that paper appears to be exactly what I was looking for:
<a href="https://i.sstatic.net/WzQLc.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WzQLc.png" alt="enter image description here"></a></p>

<p>It's also worth mentioning, maybe, that this <a href="https://www.researchgate.net/publication/297452126_A_multimodal_adaptive_dialogue_manager_for_depressive_and_anxiety_disorder_screening_a_Wizard-of-Oz_experiment" rel="nofollow noreferrer">later paper</a> seems to describe all the specific characteristics that pyAudioAnalysis analyzes: "signal energy, entropy of energy, zero  crossing  rate, spectral centroid, spectral flux, Mel Frequency Cepstral Coefficients, Chroma-based features, etc."</p>

CC BY-SA 3.0
2016-07-06T21:13:10.590Z
BTW, the emotion wheel is called the circumplex model of affect (see James Russell and Lisa Feldman Barrett).  Affect is just one feature of emotions (e.g., the wheel doesn't capture conceptual content).  Indeed, you aren't really "recognizing" emotions by recognizing valence and arousal.  You're recognizing affect.  For example, high arousal + positive valence doesn't equal excitement, but the emotion "excitement" (typically, but not always) involves high arousal and positive valence.
mrt
CC BY-SA 3.0
2016-07-06T19:36:45.977Z
2016-07-06T19:42:12.760Z
2016-07-06T19:42:12.760Z
RJB
<p>In my layman's experience, I'm vaguely aware there are four base emotions: <strong>happy, sad, afraid/surprised, and angry/disgusted</strong>.<sup><a href="http://www.theatlantic.com/health/archive/2014/02/new-research-says-there-are-only-four-emotions/283560/" rel="nofollow">1</a></sup></p>

<p>Some background:  We're training an AI to learn the difference between happy voices and angry voices.  We've had some success, by showing it 200 <strong>angry</strong> audio clips, 200 <strong>happy</strong> audio clips, and 200 <strong>neutral</strong>.  It can now reasonably tell when we're talking pleasantly or confrontationally... but the accuracy could be better.</p>

<p>Our total training dataset is made up of these audio clips: <strong>Happy, angry, neutral, calm, sad, fearful, disgust, and surprised</strong>. I think we can be more accurate by including these emotions.  </p>

<p>But this is the problem:</p>

<p>Happy/angry/neutral span opposite ends of a spectrum; like binary.  It's easy to say:</p>

<pre><code>Happy     1
Neutral   0
Angry    -1
</code></pre>

<p>That's the shape of the data we need to train a neural network to recognize 'Happy'. </p>

<p>So the question would be, is there any 'right answer' on filling in these blanks?  I've given it my best guesses below, but I'm hoping for something more scientific....</p>

<pre><code>Happy     1
Angry    -1
Neutral   0
Calm      X  (0.5?)
Sad       X  (-1?)
Fearful   X  (-0.5?)
Disgust   X  (-0.75?)
Surprised X  (0.75?)
</code></pre>

<p><sub> 
1: <a href="http://www.theatlantic.com/health/archive/2014/02/new-research-says-there-are-only-four-emotions/283560/" rel="nofollow">http://www.theatlantic.com/health/archive/2014/02/new-research-says-there-are-only-four-emotions/283560/</a>
</sub></p>

CC BY-SA 3.0
2016-06-21T19:49:11.743Z
I believe this is more of StackOverflow Question but what you could do is have  8 different output nodes in the output layer that can be either 0 or 1. With some sigmoid functions in the hidden layer and supervised learning I believe this may be the best way. What you could also do is output a probability (0 to 1) for each emotion. Then you can select the one with the highest value. I would not recommend going with your approach, because it will probably often confuse surprise with happy e.g., given their proximity. I don't believe you can arrange these emotions so linearly namely.
Robin Kramer-ten Have
CC BY-SA 3.0
2016-06-21T19:59:13.590Z
What is the evidence (or reasoning) for why happy and angry span opposite ends of a spectrum?  Which spectrum?
mrt
CC BY-SA 3.0
2016-06-21T22:42:19.063Z
To clarify further, the emotions you listed can either differ or overlap on several dimensions, including valence, arousal, and conceptual content.  What you may be distinguishing in your happy vs. neutral vs. angry paradigm is not the emotions per se, but their valence or arousal.
mrt
CC BY-SA 3.0
2016-06-27T18:29:48.017Z
@mrt The spectrum is customer satisfaction, so dissatisfied seems to translate into angry and satisfied to happy, if you agree?  We're also capturing the meaning/sentiment of the words said too, for context, but that's a separate process.
RJB
CC BY-SA 3.0
2016-06-27T18:50:53.137Z
@RobinKramer We're using pyAudioAnalysis for our black box. Not much idea how it sciences the audio.  Our hypothesis isn't necessarily to pinpoint an exact emotion, but to be able to tell like a 'good mood' vs a 'bad mood' voice -- by identifying traits of negatively and positively charged emotion
RJB
<emotion><reference-request><neural-network><artificial-intelligence>
Other emotions relative to angry/happy base emotions
RJB
# Other emotions relative to angry/happy base emotions

(Asked by: username_0 on 2016-06-21)

In my layman's experience, I'm vaguely aware there are four base emotions: happy, sad, afraid/surprised, and angry/disgusted.1
Some background:  We're training an AI to learn the difference between happy voices and angry voices.  We've had some success, by showing it 200 angry audio clips, 200 happy audio clips, and 200 neutral.  It can now reasonably tell when we're talking pleasantly or confrontationally... but the accuracy could be better.
Our total training dataset is made up of these audio clips: Happy, angry, neutral, calm, sad, fearful, disgust, and surprised. I think we can be more accurate by including these emotions.  
But this is the problem:
Happy/angry/neutral span opposite ends of a spectrum; like binary.  It's easy to say:
Happy     1
Neutral   0
Angry    -1

That's the shape of the data we need to train a neural network to recognize 'Happy'. 
So the question would be, is there any 'right answer' on filling in these blanks?  I've given it my best guesses below, but I'm hoping for something more scientific....
Happy     1
Angry    -1
Neutral   0
Calm      X  (0.5?)
Sad       X  (-1?)
Fearful   X  (-0.5?)
Disgust   X  (-0.75?)
Surprised X  (0.75?)

 
1: http://www.theatlantic.com/health/archive/2014/02/new-research-says-there-are-only-four-emotions/283560/

**Tags:** emotion, reference-request, neural-network, artificial-intelligence

> **Comments:**
> **username_1**: I believe this is more of StackOverflow Question but what you could do is have  8 different output nodes in the output layer that can be either 0 or 1. With some sigmoid functions in the hidden layer and supervised learning I believe this may be the best way. What you could also do is output a probability (0 to 1) for each emotion. Then you can select the one with the highest value. I would not recommend going with your approach, because it will probably often confuse surprise with happy e.g., given their proximity. I don't believe you can arrange these emotions so linearly namely.  
> **username_2**: What is the evidence (or reasoning) for why happy and angry span opposite ends of a spectrum?  Which spectrum?  
> **username_2**: To clarify further, the emotions you listed can either differ or overlap on several dimensions, including valence, arousal, and conceptual content.  What you may be distinguishing in your happy vs. neutral vs. angry paradigm is not the emotions per se, but their valence or arousal.  
> **username_0**: @username_2 The spectrum is customer satisfaction, so dissatisfied seems to translate into angry and satisfied to happy, if you agree?  We're also capturing the meaning/sentiment of the words said too, for context, but that's a separate process.  
> **username_0**: @RobinKramer We're using pyAudioAnalysis for our black box. Not much idea how it sciences the audio.  Our hypothesis isn't necessarily to pinpoint an exact emotion, but to be able to tell like a 'good mood' vs a 'bad mood' voice -- by identifying traits of negatively and positively charged emotion  

---

## 1 answers

### Answer by username_0 on 2016-07-06. Score: 1 | Accepted answer

As I mentioned in the comments, the tool we're using is [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis). The tool's author, Theodoros Giannakopoulos, also wrote an earlier paper "[A dimensional approach to emotion recognition of speech from movies](https://www.researchgate.net/publication/220734016_A_dimensional_approach_to_emotion_recognition_of_speech_from_movies)"

This "Emotion Wheel" from that paper appears to be exactly what I was looking for:
[![enter image description here](https://i.sstatic.net/WzQLc.png)](https://i.sstatic.net/WzQLc.png)

It's also worth mentioning, maybe, that this [later paper](https://www.researchgate.net/publication/297452126_A_multimodal_adaptive_dialogue_manager_for_depressive_and_anxiety_disorder_screening_a_Wizard-of-Oz_experiment) seems to describe all the specific characteristics that pyAudioAnalysis analyzes: "signal energy, entropy of energy, zero crossing rate, spectral centroid, spectral flux, Mel Frequency Cepstral Coefficients, Chroma-based features, etc."

> **Comments:**
> **username_2**: BTW, the emotion wheel is called the circumplex model of affect (see James Russell and Lisa Feldman Barrett).  Affect is just one feature of emotions (e.g., the wheel doesn't capture conceptual content).  Indeed, you aren't really "recognizing" emotions by recognizing valence and arousal.  You're recognizing affect.  For example, high arousal + positive valence doesn't equal excitement, but the emotion "excitement" (typically, but not always) involves high arousal and positive valence.  

---

