AI and Speech Analytics

The World of Speech Analytics and Computational Linguistics

When we speak of automated speech recognition(ASR), we all think of robots and the new age of communication where we now talk to our devices using voice commands. Our devices are now able to understand our voices and tones. This allows the “machine” to know how we feel. And from this, the algorithms in place determine what the next best step is based on the user’s response and mood gathered from their tone. Determining the next best step involves machine learning(ML). With this, we have the field of computational linguistics.

Yes, we are in the age of technology in which we interact through speech with our devices quite frequently. It is becoming the new norm. This is great for those who prefer to have a more humanlike experience with their devices and it allows for better ease of use. The world today is using this technology in all aspects of business and communication. Communicating with voice recognition systems and interacting with customers utilizing speech analytics has now become industry standard. Utilizing this technology improves the customer experience and the company’s customer service integrity.

Sure, we’ve all had the experience in which the robot on the phone doesn’t understand what we are saying and asks us to repeat ourselves several times until we get frustrated. Then, the robot recognizes our tone sounds sharper and it says, ” I seem to be having trouble understanding you, let me get you over to an agent. One moment, please”. This is the nature of how we communicate now. As we become more accustom to this process, we learn to speak more clearly and better articulate our words for the robot to understand. Meanwhile, there are people working daily on improving their speech recognition systems to better understand the speaker and their emotions. This is where AI and machine learning come in have been ramping up to take on this endeavor.

AI and Machine Learning Applied to Speech Analytics

Due to the digital transformation, AI and machine learning have paved the way for speech analytics to become more efficient and part of our everyday lives. Neural Linguistic Processing(NLP), Neural Linguistic Understanding(NLU) and neural networks have made speech analytics become a branch of AI the field continues to grow. If you are using AI and Speech Analytics, you are on the right path to keeping up in today’s ever evolving competitive market by adapting to how we interact using automatic speech recognition, AI, and machine learning technologies.

Speech recognition works by detecting a sound vibration and converting it from analog to digital format. The computer then takes the digital format and uses that as input. The input is then put through a series of complex algorithms that return the data in a text format. Then, if the preferred return communication is audible format, the text is read back to the person using text to speech conversion. The science has become so useful, it is being used in an array of products for ease of use and customer satisfaction.

AI and Machine Learning is the science in which algorithms are applied to audio segments and comprehended using automatic speech recognition and the natural language understanding. There are three forms of machine learning: supervised, semi-supervised, and unsupervised machine learning and predictive analytics. These methods of deep learning allow the software to understand the mood, sentiment, and tone of customer conversations throughout the customer journey.  This will provide insight into the customer experience and how we can improve it. Deep learning uses large data sets as reference points that are linked together and branch off of a tree of knowledge. As the systems continue to mine the data and discover more information relevant to the subject of its expertise, the machine learns and grows its knowledge base.  

Business Applications of Speech Analytics

Call centers have tons of audio recordings that can be used as data to improve the customer experience and increase customer satisfaction(CSAT). This data usually sits in an archive and is only reviewed when a customer provides feedback in the form of a complaint or praise of the experience they had. Too often, it is never reviewed and looked at unless it is brought to the attention of management through a customer survey. However, with ASR, NLP, NLU and AI/ ML combined, the data can now be identified and brought to their attention using software that has been built for exactly this purpose.

When call center agent performances or customer behaviors are analyzed, the machine references the data of common and ongoing issues, customer account history, and agent knowledge scales to make predictions and suggestions on how to move forward with the customer. Behavioral analysis is used to identify signs of discomfort through recognition of the customer’s tone and choice of words. This can also be used to better align and transfer the customer to the right agent or staff member to handle the situation. In this case, the software is personalizing the user’s experience and offering intelligent call routing. 

Predictions are based on the trends identified during the machine learning and deep learning processes.  Linear regression can be used to make predictions based on a small number of data features. Whereas, deep Learning powered systems can recognize hidden patterns that would otherwise be unpredictable by digging through the data and sprawling artificial neural networks.  Artificial neural networks utilize large sets of data by transferring the data back and forth amongst a larger network of artificial neurons used to recognize patterns the same way a human brain would work. This allows software developers to create virtual assistants and chatbots that makes extensive use of these algorithms to process the human voice and respond accordingly.

Through the use of these technologies and the software available to call centers, you can reduce hold times for customers, better manage high call volumes, reduce operator stress, and increase customer satisfaction. The technology also unlocks possibilities in Interactive Voice Response(IVR). Automatization technology can quickly deliver relevant suggestions and answers to a customer’s needs without having to involve an actual human being, providing the solution in a more timely manner and reducing the time the customer is on the phone waiting to get through to an agent.