Automatic Speech Recognition Solutions Have Become A “Must Have” Nowadays

Automatic Speech Recognition Solutions Have Become A “Must Have” Nowadays

As a business leader, you know that the foundation of your business is represented by happy customers. And they are also the key to business success if you listen to their concerns. Customer feedback helps tremendously in the evolution of the business and improves their experience.

We believe it is obvious that providing a great customer experience is the main goal for a business owner. And if the focus is on creating a positive experience, the risk of a decrease in team performance, a decrease in revenue, and customer retention is imminent.

We talked in a previous blog post about Speech Analytics and the benefits this process brings to a business. And also, in that blog post we mention that, through the Speech Analytics process, you can get a clear picture of what customers think about the services/products offered. The results obtained from Speech Analytics can provide vital information, and you can act accordingly to improve the entire customer experience.

Speech Analytics has become an essential tool for providing information and benefits to all departments in a company. Speech Analytics is implemented in call centres because they represent the initial contact of a customer with the business.

Speech Analytics in a Nutshell

If we were to place it in time, in 2002 Speech Analytics became an available resource. Since then, it has grown exponentially and been adopted worldwide. If in 2002 it was a “nice-to-have” resource, today it is a “must-have”, and this is only due to the power to change the experiences offered to customers.

In a world where data is essential, the process of Speech Analytics has grown so much that it does not just include simple telephone conversations. The solution can analyze telephone conversations, e-mail, text, and so on. It can 100% transcribe conversations and turn them into very well-structured and searchable data.

Used properly, Speech Analytics is a powerful tool!

Its Valuel

In the 2021 research “State of Automatic Speech Recognition”, it is clear that Automatic Speech Recognition is not just a new trend for many companies. 99% of research respondents mentioned that they use Automatic Speech Recognition as part of their business strategy, and this is getting the deserved attention in the years to come, coming to be implemented as a fundamental tool.

An advanced version of Automatic Speech Recognition, or ASR, in short, levitates around Natural Language Processing, or NLP. This version of ASR is the closest to allowing a real conversation between people and machine intelligence and, although it still has a long way to go before it reaches a peak of development, we already see some remarkable results in the form of smart interfaces – for instance, Siri on iPhones.

How the RepsMate team exploits the potential of ASR

Our solution is meant to understand voice and text conversations in order to return valuable insights to the business improvement process and increase customer satisfaction. Solution steps:

1. Call Transcribing​​

Speech Analytics system takes unstructured data from recorded calls, emails, and other customer interactions and matches them with structured metadata – agent name, time, day, call duration, customer identity.

Basically, the sound is subjected to the process of speech recognition and turns into text. And, at the same time, the signals for voice agitation, cadence, and silence are extracted.

Subsequently, all this data is normalized in a consistent channel format. All this information makes tracking a customer’s journey easier. Also here, the recording and transcription have the appropriate format that does not include sensitive information, for PCI compliance.

2. Conversation Analytics

The information obtained can be combined and measured through certain performance indicators – for example, agent quality, customer satisfaction, emotion, and risk of compliance. 

In order to evaluate the accuracy of the data from calls, we look at the WER (word error rate) metric, which represents how many words within an audio segment are erroneous out of the total words.  Therefore, a lower WER indicates a better ASR system. 

However, the evaluation of the ASR performance is highly dependent on a key factor: the evaluation dataset. For a straight-forward example, if we evaluate a high performing English ASR system on a dataset consisting of audio transcripts in the Romanian language, the results will be a lot worse. That is why most ASR systems state their performance related to a specific language.

Our research and experience with different ASR systems show that there are some important factors that must be taken into consideration during evaluations, which may help when considering a suitable ASR system for your company:

3. Actionable Insights

Metric information reports can easily be transformed into an action plan to improve processes.

Conclusion

It is right in front of us that the pandemic has transformed technology and business principles among industries. It has even accelerated the adoption of the ASR. Industries such as retail, banking, and healthcare have accelerated their implementation process, largely due to the global COVID-19 crisis.

Two key findings from the “State of Automatic Speech Recognition” research:

  • First, industries that in the past have adopted emerging technologies faster due to consumer demand (retail and banking, which were the first adopters of mobile applications) have quickly recognized the importance of ASR.
  • And the second conclusion is that industries that have been slower in innovation (for instance, healthcare) have accelerated ASR due to the impact of the pandemic.

Want to know more about how your organization can benefit from a simpler, more modern, and automated customer engagement?

@RepsMate we fundament our work on extensive research, providing our clients with evidence-based practical solutions that facilitate communication between reps & customers and make interactions count.
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