Deep Learning – The Future of Predictive Voice Analytics

Deep Learning Algorithms Are Becoming the Future of Predictive Voice Analytics

The concept of predictive voice analysis appears relatively simple on the surface. You use a computer to analyze a voice file and determine whether the speaker is telling the truth or not, or doing something like following a prescribed speech pattern for example. Of course, while this sounds easy in theory, it is a highly complex task in practice. Indeed, this sounds like an ideal candidate for the use of deep learning algorithms.

What is Deep Learning?

Deep learning is the next stage of advancement in teaching machines how to make intelligent decisions. Modeled after the human brain, deep neural networks have many hidden “layers”, layers which, like neural pathways in the brain, are able to make abstract insights and creative interpretations from raw physical observation. Data scientists have developed these models as a way to solve complex problems. These models often work with multiple layers of information which can any affect a particular problem.

Deep learning seems to be a natural means to delivering high quality signal-based voice analytics. Consider the cocktail party effect: given many different sources of noise, the human brain is able to process all of them and focus one’s auditory attention on a particular source (a person speaking for instance), and further be able to discern what the person is saying, interpret it, and react accordingly. We often take this behavior for granted, but for machines it is anything but simple. With deep learning methods, machines are now able to emulate the same activity and more. In a very direct way, human discernment, interpretation, and reaction correspond to business intelligence, predictive analytics, and prescriptive analytics.

Major corporations, including Google and Microsoft, use deep learning to assist with complex problems such as speech and image recognition. Indeed, deep learning is now regularly used whenever there is a need for large-scale data analysis.

Deep Learning’s Need for Large Amounts of Relevant Data

The remaining factor in being able to program software that can achieve these sophisticated feats is data. Nearly all types of predictive analytics require the need to record and process data on a large scale. Historical data is collected and manipulated in such a way that realistic predictions can be made about the future. Predictive analytics has been described as the science behind making smarter decisions.

More important than there simply being enough of it, the data required for carrying out a specific task needs to be relevant, coherent, unambiguous, and descriptive. In this age of information and the Internet of Things, we are starting to see this level of quality in the massive amount of data being collected. Simply throwing more data at a machine-learning algorithm won’t necessarily make it “smarter” either. Indeed, the wrong kind of data may even make predictive performance suffer. Businesses are starting to see the benefits of quality over quantity. It is being seen time and again that a smaller amount of data that has been cleaned, carefully considered, and processed through more sophisticated algorithms far outperforms brute force statistical methods using a massive heap of unprocessed data.

When a firm implements predictive voice analytics for the first time, it will be able to make a relatively limited range of predictions, based on general data. As time progresses, the data collected can improve the overall quality of the data set, and therefore can improve the breadth and quality of predictions. For instance, when a debt collection agency implements predictive voice analytics for the first time, it can predict customer or agent behavior to the extent that historical data and industry statistics are represented and well captured. Over time, additional data will be collected from this particular agency’s agents and customers. The predictive models will adapt to the new contextual data, and the results will more closely follow reality.

Deep Learning Implementation in the Call Center

There are some common emotional indicators and behavioral signals that a client is reluctant to pay but able. These indicators will not be identical for the clients of every firm in different business applications. As a firm applies predictive models across its client conversations a truer picture emerges regarding which emotional and behavioral indicators are most significant for that firm.

With more sophisticated computing equipment now available, there is more opportunity for complex number crunching, resulting in better predictions. Every new conversation generates new data, as does every new transaction. The general problem of Big Data then is determining how to handle such large amounts of unstructured data. This is where deep learning comes in: artificial intelligence (AI) which can take vast quantities of raw data and make sense of it.

The process of using Predictive Voice Analytics means that relevant newly collected data will constantly be added to its database, re-examined and potentially incorporated into future scenarios. Each new layer of information adds to the deep learning layers relating to an issue. Deep learning has a major place in the automation of making more intelligent decisions and can be an extremely powerful means for call centers to leverage the vast amounts of unstructured data created on a daily basis.

 

Access to RankMiner's paper on using Artificial Intelligence to monitor and measure call center agent performance