Conversational AI applications are increasingly valuable in today's society, but they face a significant challenge: most human-AI interactions still occur through text. Text-only communication can be effective in certain scenarios, but it has notable drawbacks in others. 

We use tone, rhythm, and pitch to convey words and other information. These vocal cues can reveal a person's emotions, but text-only communication obscures them. We usually talk about our feelings and thoughts, so texting is unnatural.

Adding voice to your conversational AI application is how to get around these problems. Equipping your AI system with voice technology creates a more natural user experience.

However, integrating voice into conversational AI applications has historically faced challenges (such as high error rates, audio quality problems, and accent differences). This is why text-only conversational AI has become more widely adopted and usable. 

These challenges are fading with the development of transformer-based acoustic and language models that better handle voice input and more natural speech output.

This article explores the benefits of integrating voice with text-only conversational AI applications to make them usable. You will learn:

• Use cases of voice-based conversational AI applications
• Compare voice-based AI with text-only AI and how they
• Show how these improvements can improve user experiences and lead to better interactions.

Let’s jump right in! 🚀

To date, conversational AI systems have mostly used text-only interfaces. This is mainly because modern conversational AI is based on large language models (LLMs) originally designed for text inputs. The simplicity of text made it straightforward to integrate chatbots into existing chat interfaces (webchat, SMS, messaging apps).

While text-only conversational AI has proven valuable in various applications, including customer support and virtual assistants, it has several limitations:

• No hands-free interaction: With text-only conversational AI, users must type to communicate, which is impractical during exercise activities. Voice conversational AI can allow users to track workouts, set goals, and control music hands-free.
• Slow Interaction Speed: Interaction with a text-only AI can be slow—users must stop whatever they are doing to read through lengthy outputs, which require full attention and potentially distract them from other tasks.
• Impersonal Interactions: Text does not always show how people feel, which can be frustrating. Emojis can help show tone, but they are limited and can get too many, so they do not always capture the subtleties of human expression.
• Reduced Accessibility: Text-only AI does not offer adequate accessibility for individuals with disabilities who struggle with typing. This lack of accessibility can exclude such users from effectively interacting with AI systems.
• Reduced Engagement: User engagement can decrease in text-only conversational AI applications due to the burdens of manual typing and reading fatigue.

You can address the limitations above by incorporating voice into your conversational AI application. In the next section, we will explore the power of voice and understand why it is such an effective communication tool.

Speech comes naturally to humans. A baby starts making sounds around four months, and by their first birthday, they can usually start saying words. During this time, they have already developed an understanding of the language spoken to them. In contrast, children begin to read and write between the ages of 4 and 7 years.

The fact that most people throughout history, even though they could speak perfectly, often could not read or write because they were not taught how to shows how unnatural text-based communication is.

Additionally, every civilization has created some kind of spoken language. Not all of them have made text-based systems, though, which shows that speech is more natural.

When we hear a voice, we naturally picture the person speaking. This shows that speech is more than just words; it carries extra information. People's voices are like their brands; how they talk to us shapes how we see them. The same principle applies to products using voice-based conversational AI systems.

Here are some key features of voice that make it more appealing:

• Tone: Conveys the speaker's emotions and attitude, influencing how a message is received.
• Intensity: Or loudness, emphasizes parts of a message and conveys confidence or urgency.
• Inflection: Or pitch variation, makes speech more engaging and helps convey meaning and emotion.
• Quality: Voice quality, including clarity and resonance, affects how pleasant and trustworthy a voice sounds.

Rhythm: This is the pattern of speech, including pauses and emphasis, that can influence engagement and interest.

This starkly contrasts text, which struggles to convey the same depth of information. While text can effectively communicate basic facts and ideas, it often fails to express the nuances of tone, emotion, and personality naturally embedded in spoken language.

There are a lot of different uses that can be made possible by adding voice technology to conversational AI applications. In the following sections, you will explore various use cases of voice-enabled conversational AI applications and discover how they address the limitations of text-only ones.

Voice-enabled conversational AI applications create a more natural conversation experience with humans. When an AI system is voice-enabled, people can easily connect with it and even think it has human traits, even if they know they are talking to an AI. This effect is known as the ELIZA Effect.

The naturalness of the interaction depends on two key factors: the speed and quality of the speech synthesis model used. If a conversational AI's response is slow, it disrupts the user experience, making the conversation feel less natural. Additionally, if the model doesn't sound authentic, it could put off the user.

To make a conversational AI fast, the individual components of the AI, including the speech recognition model, the speech synthesis model, and the language model, have to have low latency.

In addition to providing quick responses, conversational AI applications must support interruptibility so that users can interject while the system is speaking.

Elerian AI is a low-code visual dialogue builder you can use to create sophisticated conversational voicebots. Under the hood, Elerian’s tech uses conversational AI agents that sound like real people to make call center operations run more smoothly. 

These agents can handle incoming client calls and solve problems with minimal human help. Their ability to talk to customers naturally and fluently helps reduce the number of calls that get hung up. Deepgram’s Nova model, which has the lowest latency of any ASR model on the market, makes Elerian's AI fast and successful.

CNN reporter Becky Anderson's interview with Groq CEO Jonathan Ross, where Becky conversed with a voice-based chatbot running on Groq's AI chip, shows how important speed is for letting applications talk to humans naturally. In that interview, despite Becky’s attempts to stump the AI by speaking rapidly, the system kept pace and engaged in a smooth, natural dialogue.

When using text-only conversational AI, the user interacts with their hands, which means they can’t use them for other tasks. In contrast, voice-enabled conversational AI allows users to interact with the application hands-free.

For example, a user can drive while conversing with the application to get directions from the AI or work out and get real-time fitness guidance, all without needing to touch their device.

Real-world Example

Vapi.ai enables developers to build voice-enabled conversational AI applications with ease. With Vapi.ai, you can create hands-free solutions like fitness guidance AI, voice-activated smart home controls, virtual assistants for driving, and interactive voice-based learning tools.

In a demo project, the CEO of Vapi developed fitness guidance conversational AI that serves as a motivator, actively engaging with users during their workouts. Deepgram’s Nova powers the transcription, and Aura powers the AI's voice.

If you want your conversational AI app to be expressive and good at reading your users' emotions, voice-enabled systems are the best way. Listening to their prosody and tone, you can tell a lot about someone's feelings through their voice. 

Chatbots and conversational AI applications can tell how someone feels by listening to their voice and using an emotion recognition model to figure out what they are saying. Essentially, the application can respond with a similar level of emotional engagement. 

This capability is particularly valuable in domains where empathy is essential, such as healthcare and mental health applications. In these situations, picking up on and reacting to subtle emotional cues can improve the user experience and help the application connect with the user more deeply.

Hume.ai specializes in developing AI models that are empathetic and capable of detecting users' emotions, allowing them to respond appropriately. One of their models, Empathetic Voice Interface (EVI), which you can access through an API, uses the user's voice tone to determine the appropriate reply timing.

Deepgram's Nova-2 model is at the heart of EVI's transcription abilities. Their documentation provides more information on integrating Deepgram's technology's speed with Hume's EVI's expressiveness.

Voice-based Conversational AI applications make it much easier to convey detailed information quickly and clearly. 

Voice interactions are different from traditional text-only interfaces because they let users talk about complicated ideas and instructions in a more natural and intuitive way. This helps greatly in situations where clarity and understanding are important, like technical support, education, and healthcare. 

For example, Deepgram's Audio Intelligence API does a great job of using these models to pick up on the subtleties of user speech. This lets intent and context be extracted in real time. The speaker diarization feature allows differentiation between various speakers in a conversation.

Using the information gathered from the user’s voice, organizations can address queries more effectively, ensuring that the information conveyed is relevant and tailored to the user's needs.

OneReach.ai offers a powerful platform for building conversational AI solutions equipped with a wide range of components that enable the creation of rich and engaging experiences. To further enhance its capabilities, OneReach.ai partnered with Deepgram to integrate its advanced audio intelligence into the low-code platform.

They used Deepgram’s Audio Intelligence API to extract valuable insights from conversations. The platform automatically transcribes all interactions, enabling features such as conversation summarization and sentiment analysis. 

That ensured that organizations could respond to users more effectively and tailor their responses based on the user’s emotional state and the context of the conversation.

Personalization is key to improving voice interactions for users because it allows conversational AI systems to give relevant responses to each user. 

Voice systems can provide users with specific information and help by using in-context learning and connecting to customer relationship management (CRM) systems like Salesforce. With this integration, the voice assistant can access user data like preferences and past interactions, making conversations more relevant and interesting.

Personalization in voice interactions goes beyond just the content; it also includes the AI's features. Voice assistants usually let users pick the gender, accent, and tone of the voice, so they can talk to a voice that sounds natural and familiar. This level of customization makes the user happier and feel connected during conversations with the AI.

Daily Bots is a hosted platform for real-time voice and video conversational AI bots built by folks at Daily. With Daily Bots, you can create AI Agents that talk naturally, with fast voice-to-voice response times, interruption support, and multi-turn context management. It integrates with Deepgram’s ASR (Nova-2) and TTS (Aura) models and LLM partners like Anthropic (Claude) to provide developers with full customization capabilities for their voice applications.

Speaking and seeing together in a multimodal interaction improves the user experience by giving them an immersive way to talk to conversational AI apps. When voice interactions are combined with visual elements like images, videos, charts, or on-screen prompts, users can get more information and do it more easily.

Tavus is a platform that builds digital twins capable of interacting with users through a conversational video interface. With Tavus, users can communicate with AI as if they are on a video call, creating a more immersive and engaging experience. 

Combining visual and audio elements elevates communication to a new level, allowing for nuanced interactions that mimic real-life conversations.

Platforms like Tavus show how powerful voice-enabled conversational AI can be when combined with other methods to make the interaction more natural.

Now that we have seen the benefits and uses of voice-enabled conversational AI, let’s compare them to show how voice can improve conversational interaction.

Text lacks the flexibility of customization; it's static. For example, the text:

"Hello! How can I assist you today?"

remains the same everywhere, regardless of the context, unless you alter the font or color. But with voice, the same message can be delivered in different tones, accents, and styles, allowing for greater personalization. 

Voice cloning can take this further to enable highly customized interactions tailored to specific users or applications.

Here are a few different voices from Deepgram's Aura saying the same thing:

aura-stella-en saying:

"Hello! How can I assist you today?"

aura-orpheus-en saying:

"Hello! How can I assist you today?"

aura-perseus-en saying:

"Hello! How can I assist you today?"

Another unique aspect of voice that text cannot capture is accent. A single language can have multiple accents, each specific to a particular group. 

These accents are a powerful way to connect your application more deeply with specific demographics.

Here are a couple of examples of different accents with Aura:

aura-angus-en (Irish Accent): Top o' the mornin' to ya! How can I be of help today?

aura-helios-en (UK Accent): Cheerio, mate! How may I help you?

aura-orion-en (US accent): Howdy, partner! How can I assist you today?

Voice interaction is quickly becoming necessary for conversational AI applications to be truly useful. It lets people talk to AI in a natural and intuitive way. Many advanced components (speech synthesis, speech recognition, LLMs, etc.) need to work together smoothly to get the most out of voice-enabled systems. 

Thanks to Transformer architecture, which has become the universal architecture for acoustic and language modeling tasks, we are seeing new applications that were once considered science fiction. 

Conversational AI systems are getting close to being able to have conversations with humans. However, there are still big problems to solve, especially when understanding context, emotions, and the subtleties of human speech.

Even if we haven't achieved human-level conversations, the current voice solutions are perfect for making conversational AI systems usable for various applications. We urge you to explore these solutions and see how to integrate them into your application. 

Check out our API Playground to hear how voices make chat come alive.

What is conversational AI?

Conversational AI is the branch of AI that focuses on building intelligent agents to interact with humans. The conversation can be in the form of text or speech.

What does it mean for a conversational AI to be voice-enabled?

A voice-enabled conversational AI interacts with users via voice. It can listen to the user and produce realistic speech. 

What technologies are used in Voice Conversational AI?

The technologies used in voice conversational AI include automatic speech recognition (ASR) to convert spoken language into text, large language models (LLMs) to understand and interpret the text, and Text-to-Speech (TTS) for generating realistic speech. 

What are the challenges in achieving human-level conversational ability in AI?

Achieving human-level conversational AI involves challenges like understanding context, handling ambiguous inputs, and maintaining natural conversations. Recognizing diverse accents, tones, and emotions poses significant hurdles.