Are voice biometrics the unique gamechanger AI chatbots need?
AI-based chatbots are alleviating the demands posed by the customer service industry today. With more features being enabled through more algorithms such as speaker diarization for easy identification, segmentation, and speech analytics, more companies are realizing the full potential AI-based chatbots can bring to them.
Today, voice technology enhancing AI bots continue to see improvements. Natural language processing (NLP) algorithms are trained to handle multi-intent and multi-entity in a single utterance. This includes the ability to handle audio across multiple channels, ambient noise management, and automatic language detection to channel the right speech recognition model for decoding.
What makes it more unique is voice recognition biometrics. By making use of sounds waves and voice patterns to generate a unique identifier, voice recognition biometrics can categorize and recognize inputs based on behavioral features. This includes sentiment detection modules for deep speaker insights which include pronunciation, accent, talking speed, speech emphasis as well as physical characteristics such as the mouth, nasal passage, and vocal tract.
While there are myriad conversational AI companies now looking to make the most out of voice recognition biometrics, Gnani.ai, a Samsung Ventures company focuses more on multi-model and multi-channel communication. Their proprietary speech recognition APIs and NLU based solutions power customer support automation and analytics for leading companies across industries.
For example, traditional identity verification and authentication systems in customer service systems require users to provide PINs, dates, and passwords. This age-old method can be a cumbersome process often leading to a frustrating experience. Voice biometrics technology can help customers, employees and other categories of users access secured systems by simply using their “voice”.
Using voice biometric solutions, organizations can provide a seamless, convenient, and highly secure method for customers, in almost any industry. For example, a person wanting to check his insurance coverage for hospitalization can call up the insurance company, and have the AI bot handle his queries. Later that day, the same customer may decide to make another change and decides to send an email or chat, which will be handled by another bot.
This multi-model, multi-channel operation by AI chatbots enables customers to have a seamless experience in dealing with their concerns. Often, the bots can make the changes automatically without the need for any human intervention.
When it comes to voice calls, different individuals speak in different accents. Some speakers may even mix their spoken language with a local dialect or unique phrases. The beauty of voice recognition biometrics is that it can learn and distinguish the accents and communicate with the speaker without any hiccups. It compares various elements and patterns from a user’s voice, such as pitch, dialect, and tone.
According to Ganesh Gopalan, CEO and co-founder of Gnani.Ai, voice biometrics are going to be an important tool as automation use cases increases. He believes that AI bots are going to be more natural sounding and take up new use cases, which will see them move beyond the contact center.
“A lot of use cases that were thought to be impossible can be possible in the future. Bots can be scalable, be more efficient and work round the clock. While they may replace some jobs, there will be more use cases whereby bots will find themselves best used as well. Human customer service agents are still needed but will only focus on more important work,” said Ganesh.
The global voice biometrics market size is expected to grow to USD 3.9 billion by 2026. Factors such as the increasing demand for robust fraud detection and prevention systems across the banking, financial services, and insurance industry and the need for reducing authentication and identification costs are driving the adoption of voice biometric solutions across the world.
Voice recognition biometrics have also led to voice cloning, which involves deep-learning algorithms which allow the manipulation of existing audio. While voice cloning is expected to see more use cases in the future, there are still several legal and security concerns that need to be sorted out first before organizations can implement such technology.
“For example, you have a celebrity who is the face of a brand. Voice cloning tech can adapt to speak with the voice of a particular person. Of course, there will be legal permission to this. But apart from that, this can also lead to personalized conversations. The bots can understand the person they are speaking to and tune the answers to that. The personalization with the right voice will revolutionize the industry.”
Ganesh also added more companies will be looking to develop their own AI chatbots in the future. Creating and training the bot to understand the use case will be crucial in ensuring the bots can fully deliver the desired outcome.
24 September 2021
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