There is an increasing demand for advanced conversational AI tools. It is predicted that by 2020, 50% of search results will be performed by voice, and by 2023, there will be 8 billion digital voice assistants in use.

Conversational AI refers to technologies that users can communicate with through voice or text, such as chatbots, virtual assistants, etc., and can help imitate human interaction by combining natural language processing with machine learning and continuously improving AI algorithms. Recognize voice and text input, understand its meaning and translate it into various languages, applicable to various business processes such as customer service, marketing, etc., saving costs and improving efficiency.

A conversational AI process typically consists of three phases:

Automatic Speech Recognition

Automatic Speech Recognition (ASR) takes human speech and converts it into readable text. Deep learning has achieved higher accuracy in identifying phonemes and has replaced traditional statistical methods such as Hidden Markov Models and Gaussian Mixture Models.

NLP and NLU

Natural language understanding (NLU) takes text, understands context and intent, and generates intelligent responses. Deep learning models generalize accurately to a wide range of contexts and languages and are therefore applied to NLU. Transformer deep learning models, such as BERT (Transformer Bidirectional Encoder Representation Model), are an alternative to temporal recurrent neural networks that apply an attention technique — parsing a sentence by focusing on the most relevant words before and after. BERT has revolutionized NLU progress by delivering accuracy comparable to human benchmarks on benchmarks like Question Answering (QA), Entity Recognition, Intent Recognition, Sentiment Analysis, and more.

Text-to-Speech

TTS converts the textual responses generated by the NLU stage into natural-sounding speech. Vocal intelligibility is achieved by using a deep neural network to generate human-like intonation and clear pronunciation of words. This step is accomplished with two networks: a synthesis network that generates spectrograms from the text, and a vocoder network that generates waveforms from the spectrograms.

Conversational AI Application Scenarios

● Voice Assistants

The most common examples of AI in the world today: voice assistants like Siri, Alexa, and Google Assistant. They learn from their mistakes, using machine learning to improve responses and match intent. Using conversational AI technology means you’ll receive natural, human-like responses whether you’re asking about the local weather, finding ingredients for a recipe, or playing a song.

● In-vehicle

Drivers need to keep their hands on the steering wheel while driving a vehicle, making voice a logical solution for safely performing tasks while driving. Automakers are already using voice assistant capabilities to enhance the in-car experience; in some models, you can ask “What’s the weather like in Beijing?” and get an accurate answer right away.

● Healthcare

Conversational AI solutions are popular in healthcare, where they offer several advantages over regular chatbots. Symptom screening, triage and post-treatment care are prime examples. Patients can describe their symptoms using simple language, rather than trying to guess medical terms. Aftercare improves the quality of patient care by making it easy for patients to ask questions about treatment. Conversational AI software is also smart enough to know when to call in a medical professional.

● Customer Service

Automating support with conversational AI chatbots can improve not only the customer experience but also the employee experience. For example, instead of endlessly searching for a document, a support team can ask a bot. Teams save hours of unnecessary work that they can now spend building customer relationships.