With all new technology, the challenge is when to adopt and make the right investment decision. Do you jump before main stream adoption to gain competitive advantage over your peers or do you wait until there is broad market acceptance of the commercial value and benefits?
A recent report from Gartner, a leading technology research company, identified the fastest moving technologies in its 2012 Hype Cycle for Emerging Technologies.
Many of these technologies (see below), such as Big Data, Cloud and Social Analytics (social CRM) have been discussed in previous posts. One thing was common in that all of the technologies followed a predictive pattern on how the technology is adopted to maximise both impact and value.
Looking to the emerging technology cycle, we are at a new tipping point where a number of new technologies are converging, such as Big Data, Cloud and Social CRM. These new technologies are becoming emedded and are disrupting more established banking business models in their wake. In much the same way as when the ATMs were first introduced in the 1960s, people continued to prefer interacting with a bank clerk until dramatically in the 21st century, with the culture change of telephony, online and mobile banking, most people don’t set foot inside their local branch.
This can be illustrated by looking at one of the emerging technologies on the hype cycle.
Speech recognition has the opportunity to converge with a number of other emerging technologies, such as Cloud and Big Data, to disrupt conventional banking channels.
As we saw with the ATM, it may only be a period of time before people find it more efficient to talk to a robot rather than a bank clerk.
Surprisingly research in speech recognition predates the invention of the modern computer by more than 50 years. Alexander Graham Bell was inspired by his wife, who was deaf, to experiment with transmitting speech which ultimately led to his invention of the telephone. Not until the 1990s that computers were powerful enough to handle speech recognition.
The very essence of speech recognition can be distilled down to mathematics – you don’t need to recognise accents or dialects. James Baker, a computer speech revolutionary and founder of Dragon Systems, a leading voice technology company, identified that speech recognition had to calculate the mathematical probability of one sound following another. His algorithms have become the industry standard.
Converging New Technologies
Speech recognition technology is already commonplace in call centres, where it lets users navigate through menus and decides when calls should be handed off to a real customer service rep.
We are now entering a second technology cycle, with speech recognition being propelled forward as it converges with a number of emerging technologies such as consumerisation and Big Data to provide new commercial benefits.
All speech recognition is highly dependent on data – the more data you have, the better results you get. Google have driven this to a new level by embracing Cloud, Big Data and Data Exhaust to store every spoken or written search phrase entered into its systems. Using Big Data statistic searches, Google’s speech recognition can determine words based on its digital content.
Additional capability is delivered by Smart phones, which now have as much processing power as the mainframe machines of the ’90s. Coupled with high-bandwidth data connections to the cloud, remote servers can perform the heavy lifting required for both voice recognition and understanding spoken queries.
Using machine learning and statistical data-mining techniques, smart phones can now understand human speech. People now talk to their smart phones, asking to send email, search for directions or find information on the web – want to know more, ask Siri.
Speech is an exciting interface and can dramatically simplify interactions more than anything else. When using mobile applications, speech is the natural interface – typing will always prove to be frustrating and erroneous.
In a world where people are increasingly interacting with technology, the voice of the customer will be stored in the cloud. This data can then be accessed and analysed to provide new innovative services such as fraud detection and security biometrics to name but a few.
As an example, Sberbank the largest retail bank in Russia has utilised speech recognition in its ATMS for lie detection. By testing the customers responses to questions from a database of interrogation recording, Sberbank are able to ascertain when people are lying, dramatically improving their fraud detection rates.
For financial organisations, speech recognition is on a new and exciting trajectory. With a well established presence in call centers driving operational efficiency, the new technology wave for speech will see it become embedded with new emerging technologies such as Big Data and Cloud to create new operating models to drive greater product differentiation and customer value.
Today we can talk to computers, but very soon they will talk back.
Email : ian@IanAlderton.com
Tel : +44 (0) 7702 777770