Predictive Insights: How Deep Learning Neural Networks Can Deliver Better Call Monitoring Insights

by Natalie Chilton on Jul 19, 2017 8:10:00 AM

 

Dive Deep Into Neural Networks & Machine Learning 

Neural networks have gone in and out of style in neurology and computer science since the 1940s.

Artificial Intelligence, Machine Learning & Deep Learning; A Love Triangle

by Emily Blazensky on Jul 11, 2017 9:19:01 AM


SPEECH ANALYTICS THROUGH DEEP LEARNING NEURAL NETWORKS

At VoiceBase we believe speech analytics is a game of "what is most likely to have been said here".  Various companies have different strategies on how to answer that question with the highest degree of accuracy.

Leveraging Voice: How Deep Learning Recognition Is Influencing Data Analyzation for Businesses

by Matt Aquino on May 10, 2017 8:30:00 AM


MACHINES ARE GETTING SMARTER

No doubt you’ve already seen that for yourself: programs like Apple’s Siri and hardware such as Amazon’s Alexa have become sophisticated, eerily human technologies.

VoiceBase EU Set to Launch in April Ensuring the Protection of European Customers’ Data

by Emily Blazensky on Mar 29, 2017 7:36:10 AM


VoiceBase to process calls exclusively in region in support of Data Protection Directive 95/46/EC and the upcoming General Data Protection Regulation (GDPR).

Are Voice Analytics Truly Autonomous or Are Humans Still Required?

by Emily Blazensky on Mar 15, 2017 4:44:57 PM


VoiceBase Announces New Predictive Analytics Product; Insights

by VoiceBase on Jan 19, 2016 4:02:52 AM

Today VoiceBase is excited to announce our new product; Insights. Insights utilizes predictive analytics to match all future calls to certain call classifiers,

How To Nail Predictive Analytics By Incorporating Big Voice Predictions

by VoiceBase on Oct 2, 2014 4:57:00 AM

Predictive analytics is a branch of data mining that focuses on the prediction of future probabilities, trends and behavior. It has recently become a hot topic amongst big data fanatics and rightly so. The ability to predict future successes or failures hold value in any industry; sales, marketing, fraud detection, customer relationship management, and more. This is traditionally done by exploiting patterns found in historical, transactional and customer data to identify and optimize risks and opportunities through machine learning. The power of using machine learning capabilities to collect and organize this data is even more disruptive to an industry when you can collect different forms of data than your competitors; like spoken data.