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Conversational User Experience Back in The Days

Conversational artificial intelligence (AI) and voice-enabled interfaces are revolutionizing how humans interact with technology. From smart speakers to chatbots, natural language processing is allowing for more natural conversations between humans and machines. Two key elements that enable effective human-computer conversations are conversational user experience (CUX) and voice user interfaces (VUI). This article will provide an overview of CUX and VUI, discuss their importance in AI systems, and provide examples of their implementation.

What is Conversational User Experience?

Conversational user experience (CUX) refers to the overall experience a user has when interacting with an AI system through natural conversation. It encompasses the design of conversational flows, choice of words, personality and tone, use of rich responses like images/videos, and conveying a sense of empathy. The goal of CUX is to make interactions with chatbots and voice assistants as natural, intuitive and human-like as possible.

Some key principles of good CUX include:

  • Natural language: The system should understand free-form human language, not just rigid commands. This enables more flexible and personalized interactions.
  • Context awareness: The system should keep track of context and remember key entities to have cohesive, logical conversations.
  • Personality: The system should exhibit appropriate humor, empathy, tone, and word choice to form an emotional connection with users.
  • Helpfulness: The system should understand user intents and provide the most helpful responses, resources or actions.
  • Error handling: The system should gracefully handle errors and guide users without breaking the conversational flow.
  • Conciseness: Responses should be brief and to-the-point to not overwhelm or confuse users.

A great CUX leaves users feeling satisfied after an interaction, not frustrated. The AI system should talk, listen and respond like a thoughtful human to earn users' trust.

Importance of CUX in AI Systems

CUX is critical for conversational AI systems for several reasons:

  1. Drives adoption - If AI assistants are frustrating to talk to, people will stop using them. A human-centric CUX is key for mainstream adoption.
  2. Influences perceptions - Positive conversational experiences can make people view AI more favorably and be more trusting of it.
  3. Determines competitiveness - The AI assistant with superior CUX will attract and retain more users. CUX is a competitive differentiator.
  4. Enables personalization - Good CUX means the system can adapt to a user's preferences and provide customized experiences.
  5. Reduces effort - When done right, CUX makes conversing with AI feel easy, intuitive and almost effortless for people.
  6. Critical for complex tasks - CUX will be increasingly important as AI takes on more sophisticated dialogue tasks like customer service, teaching and counselling.

CUX requires interdisciplinary expertise in conversation design, human-computer interaction, empathy, linguistics and user testing. It should be a priority investment area as companies build the next generation of AI assistants.

What are Voice User Interfaces?

Voice user interfaces (VUI) allow people to interact with AI systems through speech. A VUI consists of automatic speech recognition (ASR) to transcribe speech, natural language understanding (NLU) to interpret meaning, dialogue management to direct conversation flow, and text-to-speech (TTS) to generate audible system responses.

VUI provides the following main capabilities:

  • Hands-free control - Users can engage with the system without using their hands, making it ideal for situations like driving.
  • Multi-tasking - Speech allows engaging with the system while doing other tasks like cooking.
  • Accessibility - VUI removes reliance on screens and buttons, providing access to people with disabilities.
  • Expressivity - Spoken language conveys emotion, emphasis and meaning more naturally than text.
  • Contextual interactions - Voice-based systems can take advantage of visual/physical contextual signals.
  • Natural experience - Talking feels more human-like than tapping buttons or touchscreens.

Some common examples of VUI include:

  • Smart speakers like Amazon Echo and Google Home
  • Virtual assistants like Siri, Alexa and Cortana
  • Smartphone assistants like Google Assistant and Bixby
  • Automotive infotainment systems
  • Call center interactive voice response (IVR) systems
  • Voice-enabled smart devices like thermostats and locks
  • Accessibility tools for vision-impaired users

Importance of VUI for AI

VUI is becoming increasingly critical for AI systems for the following reasons:

  1. Mainstream adoption - Broad consumer adoption of AI has been driven by voice assistants and smart speakers. VUI makes AI accessible in everyday life.
  2. Ubiquity - Voice interaction capabilities are being embedded into all types of devices from cars to remote controls. This makes AI universally available.
  3. Natural interface - Speech is a fundamentally natural way for people to communicate. VUI lowers barriers to AI adoption compared to other input methods.
  4. Expressive capabilities - Voice conveys nuance and emotion that text lacks. This helps make interactions feel more natural and human.
  5. Ease of use - Speaking reduces cognitive load and complexity compared to menus, buttons and keyboards for many contexts.
  6. Multimodal potential - VUI combined with visual input and sensors enables multimodal AI experiences superior to any modality alone.

As AI capabilities grow, VUI will be a preferable interface for many applications of AI technology. Companies need to focus on VUI development or risk getting left behind.

Challenges in Implementing CUX and VUI

However, there are significant technology and design challenges involved in implementing effective CUX and VUI in AI systems:

Technical Challenges:

  • Accurate speech recognition, especially with diverse accents
  • Handling speech disfluencies like "um" and "uh"
  • Interpreting intent from short spoken phrases
  • Generating natural sounding speech and inflection
  • Predicting relevant vs irrelevant conversations
  • Integrating with non-voice interfaces and touchpoints

Design Challenges:

  • Defining and adjusting personality
  • Balancing brevity with sufficient information
  • Developing natural dialogue flows
  • Determining appropriate use of rich media
  • Deciding when to hand-off conversations to humans
  • Writing dialog for thousands of potential conversational paths
  • Ensuring inclusive language that avoids bias
  • Protecting user privacy with voice data

These are active areas of research and development for the AI community. Below are some promising directions:

  • Advances in neural networks for speech processing
  • Reinforcement learning to optimize dialogue policies
  • Generative modeling to create natural language
  • New techniques in sentence embedding and user intent prediction
  • Testing CUX with real users during development
  • Multi-modal integration of voice, vision and touch
  • Federated learning to train on decentralized data while preserving privacy

As these technologies mature, CUX and VUI will become even more flexible, intuitive and human-like.

CUX and VUI Best Practices

Here are some best practices for companies working on conversational AI products with CUX and VUI components:

CUX Best Practices

  • Establish a consistent character - Give the system a clear personality. Brand tone of voice is important.
  • Design with conversation arcs in mind - Structure dialogue to have a logical flow and objective.
  • Make it purpose-driven - Ensure the AI has a clear value proposition and use cases. Avoid dead ends.
  • Test extensively with users - Conduct ongoing qualitative UX studies with real users during development.
  • Plan for scalability - CUX needs to degrade gracefully as catalogue size and users grow exponentially.
  • Integrate with other channels - Meet customers on their channel of choice (web, app, phone, store, etc).
  • Iterate based on feedback - Continuously monitor conversations and refine CUX based on usage analytics and user comments.
  • Blend AI and humans - Have trained agents ready to takeover conversations when users need human assistance.

VUI Best Practices

  • Invest in speech recognition - Error rates above 5% make voice conversations untenable.
  • Let users talk naturally - Match speech patterns by training on conversational (not formal) speech data.
  • Make it brief - Keep responses concise. Verbose text-to-speech is annoying.
  • Make intents predictable - Clarify expected speech patterns upfront to improve recognition.
  • Support hands-free use - Allow voice-only input and output when screens/typing isn't feasible.
  • Use rich responses - Take advantage of prosody, sound effects, earcons, videos and images where appropriate.
  • Make it multimodal - Combine speech with screens, touch, gestures and sensors when available.
  • Fall back gracefully - When speech input fails, seamlessly fall back to touch/text input.
  • Provide feedback - Indicate when the system is listening and confidence score of response.
  • Test with real environments - Conduct VUI testing in noisy environments with diverse users.

By following these guidelines, companies can overcome challenges and develop better CUX and VUI for their conversational AI applications.

Conversational user experience and voice user interfaces enable natural and intuitive human-AI interactions. As AI assistants handle increasingly complex tasks, strong CUX will be critical for user adoption, trust and satisfaction. VUI makes AI accessible to the masses, but excellent speech processing accuracy and natural language capabilities are required. Companies need to invest in multidisciplinary teams encompassing conversation design, linguistics, HCI, engineering and empathy to deliver the seamless CUX and VUI that consumers have come to expect. With continual advancement in neural networks, dialogue systems and user testing, the future promises even more powerful and human-centric conversational AI experiences.