The Rise of Voice AI: Transforming Human-Machine Interaction
Voice AI Overview
Voice AI is an exciting technology that will change the way businesses interact with their customers, and increase trust amongst machines to handle tasks like customer service, traditionally handled by humans. Recent developments in voice AI have enabled engineers to leverage a variety of voice infrastructure technologies to build and maintain working applications with each component serving a specific purpose:
Voice to Voice Models: Enable natural voice interactions between humans and AI
Speech-to-text (STT): Transcribes spoken words to written text
Text-to-speech (TTS): Converts written text into spoken words
Streaming: Ingests audio data to enable interactive voice recognition
Telephony: Facilitates the transmission of voice over distance through wireless communication
Each component is utilized depending on the use case it is needed for. For example, during a live call between a human and AI voice agent, the application will require a voice-to-voice model, streaming, and telephony. In an alternate example, when a business meeting takes place, an AI notetaker leverages a voice-to-voice model and speech-to-text to record notes and provides a written summary of highlights once the meeting ends. The voice AI tech stack will remain fluid with each component remaining valuable for the use case it is specifically designed for.
The Transition From IVR
Historically, and even today, companies leveraged Interactive Voice Response (IVR) technology to automatically answer incoming calls and guide callers through a series of options by using a pre-recorded voice prompt. Many customer interactions with IVR result in friction as the caller is oftentimes guided to the wrong option due to pressing the wrong button or the robotic operator mishearing the customer’s response to a prompt. Eventually, STT and TTS were introduced, enabling the use of natural language processing techniques to analyze speech and text patterns.
STT and TTS remain important for specific use cases like the ones mentioned above but have limited capability when it comes to real life customer interactions with voice AI. This is where voice to voice models come in. These models power voice applications by processing, understanding, and imitating human speech in real-time to enable natural voice interactions without the need for text conversion. Voice applications leveraging these models will transform the way humans interact with machines, how machines comprehend human speech, and more significantly, reshape how businesses manage the customer experience.
Exploring Use Cases
Voice applications powered by these models can interact with healthcare patients in the ways a human would, helping them schedule an appointment, confirm which doctor they would like to see, and record specific health-related concerns from the patient prior to the appointment. This theoretically enables clinic staff to focus on other administrative tasks, and the doctor will be more prepared by gaining instant access to the notes provided by the voice AI audio.
In another example, travel agencies can implement voice-enabled virtual assistants to provide highly personalized experiences to their clients. AI voice agents can recommend and select travel destinations for the customer, propose tailored itineraries and book activities, and share real-time updates on flight and lodging availability. Humans will no longer need to visit the agency website and book travel themselves, they can speak directly with an AI voice agent who will listen to their preferences and execute their flight, lodging, and activity requests live. Human travel agents can now focus on higher priority tasks as the AI agent handles additional call volume from new or standard customers.
Awareness Around Potential Risks
In addition to the benefits of voice AI, there are risks that can impact users, clients, and businesses. Bad actors can leverage voice AI to produce authentic speech to conduct fraudulent activity or spread misinformation. It has been reported that people have fallen victim to deepfake attacks where they receive calls from AI-enabled voices claiming to be loved ones needing financial aid.
On the technical side, voice AI struggles to understand the emotion behind a human voice. When a customer is frustrated, voice models cannot discern the customer’s emotion and are limited in their ability to how they cater to the customer’s emotion. Despite the technical progress made in voice AI today, solutions are not built to alleviate these types of situations. For now, they resort to rerouting the call to a live representative which could leave the customer waiting for a significant amount of time depending on call volume at that moment. As a result, customers are left even more frustrated, and the business exposes itself to bad reviews and lost customers.
Concluding Thoughts
The rapid development of voice AI is exciting, but this is just one instance of the innovation occurring across AI technology more broadly. Proving out use cases across a variety of industries, supported by the continuous advancement in voice infrastructure and demand for personalized customer experiences is estimated to drive market growth to ~$20 billion by 2030. Startups building voice AI solutions that exhibit these factors can emerge as market winners by altering the nature of how their customers interact with clients.
