Emotion recognition technology has come a long way in recent years, offering valuable insights into human behavior and wellbeing. One area of focus within this field with a lot of potential is building Speech Emotion Recognition (SER) models that are personalized to each user. By fine-tuning these models to the user's specific voice patterns, we can unlock a deeper understanding of their emotions and therefore build even more personalized wellbeing content recommendations.
In this article, we will explore the methods and factors involved in building personalized SER models, as well as the power of open-source models and the synergy between open source and personalization.
Personalization Through Machine Learning
Without individualized Machine Learning: This basic level relies on user-provided information. You might input your general mood, preferences, or personal details, allowing the AI to provide somewhat tailored responses. Additionally, even without advanced machine learning, techniques like moving averages can help the AI understand your emotional trends over time.
With individualized Machine Learning: At this advanced level, SER models go further in the personalization and how they are designed and built. This approach analyzes your speech patterns and emotions over time, learning from your interactions with the model. This means the AI becomes more attuned to your unique tone of voice and emotional cues, adapting over time to the individual user. It can even delve further into many of the so-called ‘secondary emotions’, such as surprise, disgust, or pride, and learn how they are connected to your tone of voice.
Factors for Personalization
Personalized SER models can consider several factors to enhance their effectiveness:
Time of Day: Emotions often exhibit fluctuations throughout the day, with individuals typically feeling more energetic in the morning and tired in the evening. By recognizing these patterns, the AI can gain insights into the temporal dynamics of emotions, enabling more accurate and tailored responses to users' emotional states.
Personal Averages: Establishing an individual's baseline emotional state provides a reference point for detecting deviations and significant changes. By understanding a user's usual emotional range, the AI can better identify and respond to shifts in emotions, offering personalized support and intervention when needed.
Physical Activity: Distinguishing between physical and emotional stress can be challenging. For instance, differentiating between feeling tired after a long day and being emotionally drained from a difficult conversation. Another example is that you can be positively high in stress-like signals and responses (e.g. very excited or very happy and in a high energy/high arousal state) or be in a very stressed or angry state - differentiating between these stress states in parallel with contextual information is possible but complex. Personalized models can learn to discern between physiological arousal states and emotional states by incorporating data from sensors like accelerometers or workout tracking tools. This integration allows the AI to differentiate between physical exertion and emotional experiences, providing more accurate insights into users' emotional wellbeing.
Displaying Personalized Information
So, how is this personalized information displayed? There are a few ways:
Highlighting Patterns: The AI can reveal trends in your emotions. For example, it might show that you tend to be more relaxed in the evenings but also highlight variations within those evenings. With machine learning, the AI can provide context to these patterns and identify trends and deviations from historical data.
Visualization: Another approach is through the use of charts and graphs. These visual representations can effectively illustrate how your emotions evolve over time. By presenting the data in a visually appealing manner, it becomes easier for you to understand and track your emotional journey. Visualizations can provide a clear picture of the changes in your emotional state and help you identify patterns or triggers.
Monitoring and Alerting: Personalized SER can go beyond passive visualization by actively monitoring your emotional state in real-time. It can provide alerts when it detects significant changes, allowing you to address your emotions proactively. At Maaind, we have multiple customer usecases that make use of this type of feature.
These methods work together to provide you with a comprehensive and insightful view of your emotional wellbeing, allowing you to make more informed decisions about your mental health.