Facial emotion recognition from your voice using Data Science

Emotion is an integral component of human behaviour and is an inherited characteristic in all forms of communication. Our experiences have trained humans to read different emotions that make us more logical and comprehensible. 

However, in the case of machines, they can comprehend content-based information like the information in audio, text, or video. However, it’s impossible to understand the profundity behind the information. 

Today, it is necessary that machines also be taught to recognize emotions more accurately to understand better and avoid communication mistakes. The present research is in emotion detection derived from audio conversations, and Audio emotion analysis has numerous applications across various industries, including banking, healthcare, defence, and IT. 

However, it is easy for text emotions to understand since there is no need for factors such as tone or pitch. However, in the case of audio emotion analysis, both aspects require attention to ensure accuracy. 

There are a variety of variables like noise, disturbance, and a variety of pauses in communication that can affect the accuracy. Creating an automated system that can comprehend the responders’ emotions is challenging.

Today, we’ll discuss the Recognition of facial emotion using your voice by using Data Science.

What’s the significance?

Facial Emotion Recognition (FER) is becoming increasingly valuable in life sciences and healthcare applications. These are significant specifically in neuroscience and in several areas like training, marketing, and quality assurance of services offered through video conferences. 

In healthcare, FER could be used in a range of intriguing applications. Examples include:

Telemedicine platforms can use AI/ML-based FER [1] to enhance their existing workflows for patient engagement to improve effectiveness and healthcare quality. FER can make it possible for healthcare professionals in mental health to become more effective. It can instantly summarise and evaluate the emotional state during a session or, more longitudinally, for an extended period. This further helps identify treatment needs and assess the effectiveness of therapy or medication. 

It will also permit health care professionals to mark up notes to document and search for subjects that have a significant emotional impact on the patient. This provides healthcare professionals a means to quickly make high-quality video calls instead of in-person visits. Therefore, learning from  Great Learning’s best data science courses and training yourself can help you take advantage of this technology and provide better patient care. 

Clinical trials are increasingly conducted in a remote and distributed manner. These trials require more personal care for participants to avoid the loss of participants and ensure that patients are engaged in the procedure. AI/ML-based FER gives a foundation for assessing the engagement of patients quickly so that clinical trial directors can better focus their efforts on preventing dropping out. Like telemedicine-based treatment for mental illness, the analysis of video of patients can reveal the patient’s responses to treatments at a minimal cost.

Insurance payers can utilise FER to evaluate the mental and emotional health status that their customers are in. The emotional state is closely linked to other outcomes related to health. Notably, in a value-based healthcare system, the capacity to recognize patients with emotions that are causing concern can speed diagnosis and intervention. 

Solution Overview

There are three kinds of features that a speech has. These are:

  •  The lexical features (the words used)
  • Audio aspects (sound properties such as pitch and tone, jitter, etc.).
  • The visual features (the expressions that the speaker uses) 

The emotion in speech recognition is resolved by studying any of the characteristics. If you decide to go with the lexical characteristics, it would require the transcription of the speech. Besides that, it will need another step in the extraction process of speech text to predict emotions from live audio. Studying visual features requires additional video recordings of conversations. 

It could not be practicable in every situation. The analysis of the audio features could be performed in real-time when the conversation is occurring. The reason is that we’ll need only the audio information to complete our goal. Therefore, we opt to look at the acoustic aspects in this study.

Additionally, the way emotions are represented can be accomplished by two methods:

  • Dimensional Representation: Representing emotions using dimensions like Activation, Valence (on the negative scale to a positive one), Energy or ( low-to-high scale), and Dominance ( active/passive scale)
  • Differential Classification: Classifying feelings with distinct labels such as sadness, anger, boredom, and more.

Each approach has its advantages and disadvantages. Dimensional approaches are more complex and provide more context for prediction.  

However, it is more challenging to implement, and it lacks audio files that are annotated in a dimensions format. 

The discrete classification approach is simpler and more simple to implement. However, it cannot provide the context that dimensional representations provide. You can use the discrete classification method to address the absence of annotated dimensional information in the general public domain.

Important Points To Keep in Mind Before Running  A Test Model

  • An amalgamation of CNN-1D (shallow and deep) and CNN-2D built on a soft voting system may produce the most effective results when recording user audio.
  • The model may confuse some low energy emotions, including sadness, boredom, and neutral.
  • ‘It may get confused between disgust and anger.
  • If few words are spoken at a higher pitch than the other words, mostly near the beginning or at the end of a sentence, it may be classified as a sense of surprise or fear.


Many researchers are studying this field because of its immense significance. In reviewing the extensive literature, researchers discovered that they carried out most research in analyzing emotion in text. 

The audio emotion analysis requires a lot of research and improvement in terms of precision. Artificial intelligence has helped to enhance the current systems. Currently, deep learning systems can learn better and comprehend. The biggest challenges, such as decoding emotions from the regional languages, need study. 

Delving into this domain requires proper knowledge and training, and taking an advanced course can help you understand the concepts involved. Therefore, you should sign up for an Master in data science course from Great Learning  to tackle the challenges with the right approach. 

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