Unpacking ASR and NLP  – their differences, challenges and benefits

Kiren Kahlon

November 1, 2022

9 min read

Our lives are being transformed by Artificial Intelligence (AI) and a big part of that is due to Automatic Speech Recognition (ASR). Speech recognition tools have made their way into homes and businesses, with global leaders like Amazon, Google, Microsoft, and IBM dominating the market. Since the capabilities and applications of AI are so vast, there is unsurprisingly a lot of terminology involved. In this article, we will be unpacking two key terms within Conversational AI — Automatic Speech Recognition (ASR) and Natural Language Processing (NLP).

What is ASR?
Put simply, ASR is the process of converting speech into text and can also be known as speech-to-text and voice recognition. It is a critical part of Conversational AI and is most often used in user-facing applications such as virtual assistants, note-taking and captioning/transcribing.

What is NLP?
NLP is the branch of AI that enables a computer to understand, process, and generate language. While the primary function of ASR is to convert speech data into text data, NLP deals with the interaction between humans and machines using a natural language such as English, simplifying the process to make it less time consuming for the end-user.

The challenges

Now that we have covered what ASR and NLP are, it’s also important to understand the challenges they face and how they are used in businesses today.

Accuracy
Conversational AI technology has come a long way but a huge problem that still persists with voice recognition is accuracy. A survey by Statista revealed that 73% of businesses consider a lack of accuracy as the leading barrier stopping them from adopting voice technology. Pushing towards human accuracy levels is a huge challenge for ASR and NLP and it’s unsurprising when you consider how complex language can be, from different dialects, to slang, pronunciation and accents.

Background noises
Another big challenge for voice solutions is noise. It is everywhere and often unavoidable, so it becomes the job of the speech recognition tool to accurately collect the speech input even when there is background noise. This can be especially tricky if there is cross-talk or other conversations going on at the same time.

Costs and deployment time
Deploying voice AI systems can often require a lot of time and money. Deep learning models are (unfortunately) notorious for requiring a lot of compute power to train, and businesses are not often prepared for the amount of time, resources, and capital involved in building, testing, and deploying the system. Training language models are complex and therefore take time and require expertise, making scaling up a nightmare.

Industry-specific jargon
Different industries use different languages, therefore different models will be required for different industries or areas of the business. For example, a model for Healthcare would use different language to a model for Construction, plus industries with very specific or complex language would probably need to build or train their own model.

There are also a couple of language-specific challenges that especially impact NLP, which we have highlighted below.

Ambiguity
Even in human interactions, there can be cases when a person is misunderstood if they are too vague or do not provide enough context. Similar to what you would do in a human to human conversation, the NLP system has to look for context that will help it to understand the phrase. Alternatively, it may need to seek clarification from the user.

Slang and different dialects
Words can mean different things in particular countries or regions. Add expressions, idioms and slang to the list and you can have thousands of different meanings. These variations of language present a number of issues for NLP systems, since some of these may have no actual dictionary definition, we are taught them in our everyday conversations. Plus, language evolves constantly, new words and slang pops up on a daily basis, which means custom models need to be updated regularly.

The business benefits

Since Conversational AI technology is ever-evolving, improvements are consistently made to make it function better. With that being said, there are a huge number of opportunities and benefits on offer to businesses when adopting voice-based solutions, which we explore in more depth below.

Increased productivity
The primary benefit of Conversational AI technology for business processes is improved productivity. Talking is much faster than typing and by integrating voice-based solutions, businesses allow their workforce to perform tasks such as form-filling or data logging using their voice. As well as being quicker to complete the paperwork, think about how much additional time is saved from having to locate these documents or waiting for this information from colleagues. Removing the need for pen and paper and outdated hardware allows your workforce to work faster and more efficiently, and ensures business operations run smoothly and consistently.

No geographical limitations
The COVID 19 pandemic highlighted the need for remote tools when much of the workforce was no longer face-to-face. This is the norm for field teams, with workforces being spread across various locations on a daily basis. Voice-based solutions are not limited to offices or specific hardware; they are mobile and accessible whenever, wherever. This is beneficial for many reasons, from connecting colleagues who are in separate locations to tracking the progress of a job on the field.

Improved worker and customer satisfaction
When smarter solutions are in place, both workforces and customers feel the impact. The increased ease and speed of completing tasks leads to higher staff satisfaction, retention, and attraction. In turn, having more knowledgeable and high-performing workers means they are able to be better at their jobs, benefiting customers by giving them a better experience.

While these benefits are applicable to voice-based applications as a whole, there are a few additional benefits that come with the NLP part. These include understanding your target market better, performing large-scale analysis and getting real-time, actionable insights and data.

The way we work is changing

As we have outlined, the business benefits of using ASR and NLP are huge. While many industries have yet to recognise their capabilities, plenty of sectors already have. Industries such as Financial Services, Banking, Telecommunications and Healthcare already heavily rely on voice-based solutions and are expected to increase their usage within the next couple of years, with no signs of slowing down.

It is now up to other industries to decide when they are going to take action, as the business world is quickly moving away from pen and paper and old legacy hardware and instead turning to smart and agile solutions that transform the way we work.

Empower your workforce

Adopting voice technology makes things easier for the business and helps propel your organisation into the future. The business world is moving fast and with so many solutions out there, finding the right ones for your business is more important than ever.

At Wluper, we are working on ​​unique voice-based Conversational AI technology, giving you access to powerful voice and language understanding technology on any device. Using advanced NLU, we allow users to speak more naturally than ever before. No scripts or patterns, simply speak as you normally would and our voice-based technology will understand.

Learn more about our TrueUnderstanding™ API or book a live demo to see Wluper in action.

‍‍Follow us on Twitter and Linkedin for the latest updates on what we’re up to.

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