A vital part of data analysis and knowledge exploration is to find out patterns in the data and have useful information out of it. The voice analytics technology is an advanced part of data science wherein raw sound or audio data is gathered and knowledge is derived out of it. Of course, it has got very different structure and processing formats than that of text mining since the acoustic features are really different and has to be processed in special way. But why to go for this voice analytics?
Consider an example where the news reporter is taking bites of people who watched the first day first show of any particular movie and asking for their reviews. Then the characteristics (e.g. pitch) of voice of people talking there itself may describe their feelings. For instance, if the movie is very bad then the voice may be of different kind showing their sadness, or if the movie is very nice, then the happiness can also be identified by observing the sound characteristics. This is just a hypothetical scenario explained for simplicity where voice analytics may be useful, but in real time there are many business cases that actually might get very important business value.
As we know, with text analytics, the actual value is not just searching a keyword or phrase, but in understanding the context in which that key word or phrase is used. For example, key word spotting may tell you when a competitor’s name is mentioned, but what you really need to know is why that competitor’s name is mentioned. The same rule applies for voice analytics. Here, the real value is not just finding the audio samples, but in conceptualizing the hidden information that in turn helps in to find out the wisdom in it. Hence these points are worth to note and critical for deriving real business analysis out of a raw sound. In reality, this technology is still evolving if one sees the development done and features explored till now.