As per current trends and reports, 41% of adults use voice search at least once per day. This means that voice-based interactions are steadily on the rise.
A significant contributor to the upward trend in voice searches is the advent of smart home devices. By ensuring that the responsive voice coming from the smart home devices remain human-like and empathetic, users are more inclined to interact with them.
In a detailed report published by the American Psychological Association (APA), we can see that even though technology has increased the number of communication channels between humans, voice is still preferred. This is because it lends a more intimate experience to interactions and helps create stronger bonds.
How do Voice Bots work?
Conversational Artificial Intelligence (AI) is a software solution that engages with customers through email-based bots, chatbots or voice bots. Voice bots interact with customers by analyzing their vocal input and formulating the most accurate response by encoding and decoding spoken content. With the help of Machine Learning, the system can train itself to continuously improve its accuracy.
On the surface, this might seem very linear. However, various components go into this technology to ensure accuracy and speed.
Automatic Speech Recognition
Automatic Speech Recognition or ASR forms the backbone of Voice Bots, as it’s directly responsible for converting spoken verbatim into text. When a caller speaks to an AI Voice Assistant, the ASR component uses an audio feed to transcribe the voice message into a wave file. The wave file is then filtered to remove background noises and other disturbances, after which it is then broken down into phonemes. Phonemes basically define how words sound, and by linking them together, the ASR can deduce what the caller has said.
Natural Language Understanding
Natural Language Understanding (NLU) is a sub-branch of the larger topic of Natural Language Processing (NLP). NLP deals with the entire spectrum of functionality for voice bots, where it interprets input, decipher the meaning and formulates responses. NLU plays a vital role in this process, by helping the algorithm identify intent and tone. In other words, NLU helps the AI-powered virtual assistant distinguish conversational elements and take the interaction forward.
Conversation Module for Correct Responses
A well-defined conversation module allows the user to effortlessly interact with the voice bot without having to follow a directive course (like in the case of IVR systems). The entire style of interaction revolves around the user’s requirement and intent, and subsequently retrieving relevant information to help the user.
Text-to-Speech or TTS is the component that ‘reads aloud’ the text that is visible on a computer screen or digital interface. In other words, the system uses various Deep Learning techniques to read a response and mimic a human voice when reading it aloud to the user. When the NLP-NLU component analyses the input from a user, it formulates a relevant response that is fed into the TTS component. The output from the TTS is what the user hears.
How do Voice Bots Comprehend Complex Languages and Accents?
Global adoption of voice-based technologies has established ‘Voice’ as a preferred medium amongst customers, and the numbers speak for themselves. However, a common roadblock that most Conversational AI solution providers endure is the process of training their interactive voice bot for user engagement.
Today, there are over 7,000 different languages and dialects. Yet, this isn’t the real problem. If we were to take one single language, we’d soon learn that this one language is spoken differently across the world.
The English language is the biggest example of this problem. English is spoken across all 195 countries in the world and is the official language for 67 of them. That means there are over 100 different accents for the English language alone! For each accent, there are a definitive set of phonemes that makes it difficult for the AI-powered voice bot to comprehend.
With the Help of Speech Recognition Optimization
Speech Recognition Optimisation is a branch of Computer Science that deals with computational linguistics. It helps the AI understand specific languages and accents by benchmarking them against an existing database of knowledge. With the help of a Voice Biometrics solution, the AI can quickly identify the user’s accent to begin processing conversational input.
By Implementing Pre-Trained Multilingual Speech Encoders
With the help of modern machine learning models, text-to-speech bots can be trained with billions of conversations to help create a strong foundation for the NLP component. For example, Gnani.ai is continuously strengthening its foundation by training its model in 20+ international languages. As opposed to building new models for multiple languages, we can now deploy any language on-demand in a few days.
By Utilizing a Multilingual Value Extractor
Value extraction and its degree of accuracy could make or break your customer experience (CX) goals. When a customer interacts with an AI voice bot, certain crucial values regarding name, address, reference numbers etc., are extracted irrespective of the language used. If a person is talking to an AI voice assistant in French, crucial values might be in English (Age, Phone Number). Hence, the Value Extractor must be trained to analyse languages and decipher critical data.
What lies ahead?
The global market size of AI-powered virtual assistants will be worth over USD 1.3 Billion by the end of 2024, and trends point towards voice-based assistants owning over 50% of this share. Hence, it’s clear that the power of ‘Voice’ will take over business processes and customer engagement initiatives.
If you’re interested in learning more about how you can leverage voice for your business, talk to us.