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How Conversational AI Platforms Utilize Top ASR Tools

Conversational AI platforms are on the rise. As more and more businesses move towards conversational interfaces, the need for platforms that can handle these interactions grows. And their impact is being recognized. In this year’s State of Voice Technology 2022 report, 54% of respondents said that conversational AI is the most impactful use case in speech tech today.

In this blog post, we’ll take a look at conversational AI platforms and how they utilize top automatic speech recognition (ASR) tools to delight customers with new experiences. We’ll also explore some of the problems these platforms solve and some of the most common use cases for conversational AI, as well as how ASR powers them.

Before we start looking at how speech-to-text tools can help power conversational AI, let’s take a look at what exactly conversational AI is, and how it helps businesses.

What is Conversational AI? 

Conversational AI is a type of artificial intelligence that enables computers to communicate with humans in natural language. Rather than forcing humans to learn what kind of commands the system can accept (like pushing a button for a specific service), conversational AI lets people speak normally, and replies in kind, just as quickly as another human would.

Conversational AI is a direct evolution of interactive voice response (IVR) systems, where callers push 1, 2, or 3, or say basic information like their account number as part of their interaction with telephone trees. But conversational AI is far more advanced. By creating a voicebot or a virtual assistant, conversational AI allows the system to respond intuitively and naturally, rather than being constrained to following specific paths down a tree.

These technologies allow customers to interact with a company to solve their own problems, rather than requiring that a customer service agent answer every call. Imagine if navigating through a phone tree were as easy as explaining what you needed—that’s the goal of conversational AI.

Why is Conversational AI Beneficial?

There are a number of ways that conversational AI can benefit businesses today, especially given the complex financial environment. Although it can be challenging to create a great conversational AI experience, the benefits make it absolutely worth the effort. Let’s look at some of the benefits that conversational AI can have for customers as well as the direct benefits that it can have for enterprises.

Customer Benefits of Conversational AI

Here are three of the biggest benefits for customers when it comes to using conversational AI platforms.

1. Shorter Wait Times

One of the biggest benefits for customers when interacting with a company that’s using a conversational AI platform is shorter wait times for issue resolution. Because conversational AI platforms can handle multiple calls at once, and can solve many of the most common reasons people might call, they end up doing a lot of the work, significantly speeding up time to answer. And, even if you do end up needing to speak to a human agent, you’ll likely to spend less time waiting there, too, since the conversational AI platform is dealing with lots of the calls that would otherwise bog down human customer service agents.

2. Choice for Engagement

Being able to contact a company on your terms is important to many customers. You might prefer a specific avenue, whether that’s Twitter, documentation, help articles, chatbots, or text messaging. Conversational AI provides another avenue that’s quick, easy, and doesn’t require talking to another person—while also keeping human agents close at hand should they be needed to solve a complex problem.

3. Faster Issue Resolution

Because conversational AI platforms can be built to handle the most common issues—and because they can handle multiple calls at the same time—they result in faster resolution times for customers.

Enterprise Benefits of Conversational AI

The benefits of conversational AI extend beyond just what customers see, however. There are a number of direct business impacts that using a conversational AI platform can have on your business.

1. Cost Savings

One of the main benefits of conversational AI is that it allows businesses to automate tasks that would otherwise require human interaction, thus saving costs. This can free up employees to focus on other tasks and improve efficiency. Additionally, conversational AI can help businesses save money on customer service by providing a self-service option for customers.

2. Increased Productivity

Conversational AI platforms can power dramatic increases in productivity, as they allow companies to handle more calls, more quickly, with less human customer service agents. With the right infrastructure in place, conversational AI platforms can handle thousands of calls at the same time, allowing for the resolution of more issues more quickly with less human agents.

3. Increased Agent Satisfaction

In addition to increased productivity, using a conversational AI platform can also increase the satisfaction of the humans who work in a call center. Why? Because the conversational AI platform handles all of the simple, rote issues like taking payments and updating addresses, and leaves humans to deal with the more complex cases that are more challenging—and more satisfying—to solve.

4. Stronger Customer Service

Effective customer service is one of the ways that companies can help retain customers—and, on the flip side, poor customer service can result in customers really disliking your company. By providing conversational AI solutions, customers will come away happier with their interaction with your business, for the reasons discussed in the section above.

5. Better Insights and Knowledge

Conversational AI platforms provide a way to capture the conversations that your customer service agents are having with customers, which can provide an excellent starting place for understanding what your customers want and need. By turning to this database, you might be able to identify patterns that help you improve how information is presented on your website, create training materials for human agents, or even build new automated workflows for your conversational AI platform to handle.

State of Voice Technology 2022

State of Voice Technology 2022

This report provides a snapshot of the current landscape of voice tech—and what’s coming in the near future—based on a survey of 400 decision-makers across the enterprise. Learn what’s happening now and what’s coming in the future.

Download Now

Top Use Cases for Conversational AI

Conversational AI platforms are being used across industries for a variety of different use cases. The below examples are some of the most common, but they’re far from the only places that conversational AI is having an impact in business today.

1. Call Centers

Perhaps not surprisingly, one of the most common use cases for conversational AI is in call centers. Using a conversational AI platform to help customers do tasks like change their addresses or make a payment without needing to talk to a human—and without needing to punch in numbers on a keypad. Companies like Elerian AI have built a conversational AI platform that uses natural language understanding, alongside ASR, to let companies create a voicebot to easily answer customer queries.

2. Healthcare

The healthcare industry is using conversational AI to automate common tasks to help doctors, nurses, billing, and patients all stay in sync. For example, conversational AI platforms can be used to follow up with patients after a procedure to check in on how they’re doing, if they’re adhering to medication guidelines, scheduling needed follow-ups, as well as gathering any other information doctors might need. Conversational AI can also be used to track mental health metrics like stress and depression with a simple phone interview.

Outbound AI is a company that’s pioneering the use of conversational AI for healthcare, offering solutions that support medical billing, streamline systems to eliminate duplicate work, and bring conversational analytics to the healthcare industry.

3. Virtual Shopping Assistants

Virtual shopping assistants are another domain where conversational AI platforms are being used. Companies like Vocinity have created virtual shopping assistants that interact with customers just like a human employee would, whether online or in stores, providing a personalized shopping experience for customers.

4. Food Ordering

One use case you might not have considered for conversational AI platforms is food ordering. For example, Valyant AI has created a platform that lets customers order in drive-thrus automatically. Kea is a similar system that works via telephone calls to take orders and even upsell customers.

End-to-End Deep Learning Automatic Speech Recognition for Conversational AI Platforms

In order to understand and respond to humans, conversational AI platforms need some way to interpret human speech. This is where automatic speech recognition (ASR) comes in. ASR is a technology that enables computers to convert spoken language into text which, with the help of tools like natural language understanding, can be used to determine what a person is saying and respond in kind.

There are a number of different ASR solutions available, each with its own advantages and disadvantages. The most important thing to consider when choosing an ASR solution is making sure you’re using one that’s well-suited for the task at hand.

In the case of conversational AI, that means using a speech-to-text solution that can provide responses in real time, and not every ASR provider is able to provide that kind of turnaround on transcription. If it takes seconds or minutes to reply to someone using a conversational AI platform, you’ve left the realm of anything that seems conversational.

The best way to ensure that you’re getting a fast turnaround is to use an ASR solution like Deepgram that’s built on end-to-end deep learning. Deep learning ASR systems are faster than other options, providing responses quickly enough that the interactions truly feel like a conversation.

Another advantage of end-to-end deep learning speech-to-text systems for conversational AI platforms is that it’s quick and easy to train a new model based on your data. That means you have a model that knows exactly what your audio looks like, including things like background noise and specific jargon or industry terms, and can far exceed generic, out-of-the-box ASR, which will never be good enough for conversational AI.

How Deepgram Works

How Deepgram Works

Want to get more value out of your call center data, build the next game-changing voice feature, or save a lot of money on speech transcription? Learn why Deepgram is the platform to get you there.

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Benefits of Top ASR Tools for Conversational AI

There are a number of benefits that the top ASR solutions for conversational AI can bring to your business. Overall, ASR can be a powerful tool for conversational AI platforms. It can help conversational AI platforms understand human speech, power other features of the platform, and improve the accuracy of the platform.

1. Speech Understanding

ASR can help conversational AI platforms understand human speech. This is the most important thing for conversational AI platforms, as they need to be able to understand what users are saying in order to respond accordingly—in fact, this is the lynchpin that holds conversational AI platforms together, and it call comes from having an accurate speech-to-text system that will work for conversational AI.

2. Call Transcription

ASR can be used to power other features of conversational AI platforms as well. For example, ASR can transcribe customer service calls or sales calls. This transcript can then be analyzed to help improve the quality of the conversation by identifying areas for improvement or training, or even provide opportunities for upselling.

3. Handling Complex Audio

ASR solutions can help conversational AI platforms handle different accents, regional dialects, or noisy environments. This is important because conversational AI platforms need to be able to understand speech from a variety of different people. As noted above, using an end-to-end deep learning ASR solution can help you in this regard by letting you tailor a model for your specific audio data.

4. Searchable Data

Using ASR with conversational AI also lets you create a database of transcribed audio of the conversations that you’re having with your customers. This can be used to power internal initiatives, from additional training, to streamlining processing, to customer education—the options are nearly endless once you have a large database of these conversations to work with.

5. Improved Accuracy

ASR tools can help improve the accuracy of conversational AI platforms by providing more transcribed data for the platform to use. And with the latest advances in ASR models, the accuracy can be high even for rare words. For example, Deepgram’s enhanced model has a broader vocabulary than other speech-to-text models and can transcribe words it hasn’t seen before.

6. Custom Model Training

Another advantage of using an ASR solution that’s built on end-to-end deep learning is that its flexibility lets you train a custom model quickly and easily thanks to transfer learning. Your business might have lots of unique vocabulary and acronyms that an off-the-shelf model struggles with. By training a custom model on your own data, you can drastically improve the quality of transcripts that the model outputs, as it now knows your lingo.

Wrapping up

If you’re looking for an ASR solution to power your conversational AI platform—or looking for a speech-to-text tool for another project—please reach out with any questions you might have and we can help you decide if Deepgram is the right fit for you.

If you’d rather dive in and get started yourself, you can sign up for Console and get $150 in free credits to give us a try, or check out our ASR comparison tool to get a sense of how we stack up against Big Tech—no signup needed.

ASR Comparison Tool

ASR Comparison Tool

Curious how our speech-to-text solution compares to Big Tech? Give our comparison tool a try to see how we stack up on different kinds of audio when compared to Amazon and Google on metrics like speed, accuracy, and more!

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