Speech Analytics is the process of analyzing recorded spoken customer interactions to extract customer sentiment and intent. This can be done through manual review or using software that automatically analyzes the audio recordings.
Speech Analytics can also be used outside of the text analytics industry, for example in linguistics research. In this case, Speech Analytics may refer to the process of automatically analyzing spoken language data in order to extract linguistic features. This may be done for a variety of purposes, such as studying the effect of accents on speech recognition or measuring the use of certain words and phrases over time.
How Speech Analytics is Used
Speech analytics can be used to measure stress in someone’s voice. This can be done by analyzing the acoustic features of the voice, such as pitch and loudness. Stressful speech tends to be higher in pitch and louder than non-stressful speech. Speech analytics software can automatically analyze a recording and output a stress score for each section
Benefits of Speech Analytics in Customer Service
- help to identify customer needs
- can be used to automate customer service
- reduce costs associated with customer service
- improve customer satisfaction
- satisfaction
Drawbacks of Speech Analytics
- require a large amount of data for training and testing
- computationally expensive
- may not be able to accurately identify stress in all cases
Speech Analytics vs. Speech Recognition
Speech Analytics and Speech Recognition are two similar but distinct fields. Speech Recognition is the process of converting spoken language into text. This can be done using a speech-to-text engine, which converts the spoken words into text that can be read by a computer. Speech Analytics, on the other hand, is the process of analyzing recorded spoken customer interactions to extract customer sentiment and intent. This can be done through manual review or using software that automatically analyzes the audio recordings.
Speech Analytics vs. Voice Recognition
Voice Recognition is similar to Speech Recognition, but Voice Recognition focuses on identifying the speaker, rather than converting the spoken words into text. This can be done by analyzing the acoustic features of the voice, such as pitch and loudness. Voice Recognition systems are often used for security purposes, such as to unlock a device using a specific person’s voice.