Emotional Analysis: Filling the Gaps of Speech Analytics

In the world of call center analysis, the most common analytical tool used by call centers is speech analytics. These analytics tools parse human speech to pick out individual words and phrases. In many ways, speech analytics software can be categorized as a semantic analysis tool, one that tries to understand spoken language.

Theres just one problem: human communication is much more complex than simply picking the right words.
Many common phrases in the English language can change meaning drastically based on factors such as the tone of the speaker and the greater context of the conversation. Many semantic analysis-based systems struggle with things such as context cues, homophones, and slang terminology.
So, while a semantics analysis system might be called a means of understanding language or meaning, it can easily miss the meaning behind specific word choices because of a lack of context.

How Emotional Analysis Predicts when Semantic Analysis Misses

Emotional analysis takes a completely different approach to the study of interpersonal communication. Where semantic analysis systems focus on what is said to try and interpret meanings, emotional analysis focuses on how individuals speak in a conversation to find the context behind the words.
By analyzing how people speak in a conversation, emotional analysis systems can provide insight into a speakers emotional state and behavior that a purely semantic analysis would not pick up on. This allows emotional analysis-based systems such as predictive voice analytics to establish what a speakers mood is during a call and even predict future reactions.
Predictive voice analytics uses machine learning algorithms to quantify emotional states and behaviors based on specific voice features that have been put through digital signal processing. Once the subjects tone of voice and emotional state have been analyzed, that information is used to populate predictive formulas that assess likely future outcomes.
For example, debt collection agencies can use this emotional analysis of a debtors tone during a first-contact call to assess how likely the debtor is to agree to pay on a follow-up call. By arranging all contacts in order from most likely to pay to least likely to pay, predictive voice analytics software can vastly improve the targeting of second-call efforts, driving increased liquidation rates for the call center.
This is something that semantic analysis alone cannot do.
While semantic analysis can be invaluable for call center operations, especially for compliance purposes, there are some areas of understanding that word choice alone does not communicate. With emotional voice analysis, you can get more contextual information to a conversation, helping to drive understanding and more accurate, powerful predictions of future behaviors.

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