Modern executives can access more data than ever before. Yet, doing so is not always easy.
Consider the contact center; many struggle with capturing and processing customer voice calls.
The inability to do so and analyze voice interactions limits the capacity of organizations to understand customer struggles, sentiment, and intent.
Luckily, speech analytics software is improving, turning call recordings into a well of business insight.
Now, the technology can even work in real-time, enabling quick, data-driven decisions.
A Definition of Speech Analytics Technology
Speech analytics utilizes automatic speech recognition, natural language processing (NLP), and other AI technologies to convert audio recordings into text and then actionable insights.
Some variants of speech analytics technology can also work in real-time to generate insights even as the call is ongoing.
Yet, no matter whether the speech analytics technology offers real-time or historical analysis (or both), the technology consists of three central components:
- The phonetic technique employs the phoneme as the fundamental recognition unit to transform unstructured conversations into a stream of language constructs that can be searched.
- Large-vocabulary continuous speech recognition (LVCSR) uses words or a collection of terms to match the audio and identify problems – LVCSR commonly drives speech-to-text conversion.
- Extended speech emotion recognition is the most complex of the three, employing neural network classifiers to assess emotion and provide predictions.
According to Reports and Data, the technology will have a $4.24 billion global market by 2026 as businesses seek new opportunities to better understand their customers.
How Does Speech Analytics Technology Work?
Speech analytics harnesses AI to perform the following tasks to transform raw audio into actionable insight.