Artificial intelligence (AI) and machine learning (ML) have become so useful and prevalent that we use them in our daily lives without really thinking too much about it. One key area where these intelligent technologies have progressed in leaps and bounds—almost to the point where they match human abilities—is the field of automatic speech recognition technology.

Today’s automatic speech recognition (ASR) engines allow us to speak to a computer or device that interprets what we’re saying, in order to respond to our question or command. This type of technology has a vast number of applications in our homes, as well as in industries such as business, banking, marketing, and healthcare.

The ubiquity of speech recognition technology is now measured globally, with an ever-increasing impact on that worldwide economy. The voice recognition technology market was worth close to $11 billion in 2019 and is forecast to expand by nearly 17% by 2025. [1]

Catering for diverse users

As demand for voice tech grows, providers of automatic speech recognition (ASR) software are increasingly compelled to develop more innovative speech recognition products that perform better and meet a more complex range of needs.

The users of speech recognition technology are not only growing in number, but also becoming more diverse, fragmented and heterogenous. This shift is compounding one of the greatest challenges faced by the developers of ASR engines, which is navigating the different dialects that exist within a single language. Native English speakers, for example, may use Southern American, British, Australian or South African dialects—each with their own accents and variations in vocabulary and grammar. The best performing ASRs are those that are highly attuned to these distinctions.

If an ASR engine is driven by robust AI and ML capabilities, it will be able to transform the spoken word from a variety of languages and accents into readable, understandable text. It will also be able to continually recognize new dialects and accents within a single language model.

A proliferation of channels

Another challenge to navigate is the increasing number of communication solutions available to users today. Within a bank contact center, for example, customers expect to access self-service using voice through a choice of channels that could include traditional telephony lines, mobile devices, web-based apps and more.

Greater expansion to the cloud is also driving the increased use of non-traditional channels. Ideally, ASR technologies should deliver a seamless service on premises and in the cloud, as the pace of migration to the cloud is often governed by the needs of the business.

To navigate these dynamics, any software company that’s in the business of speech recognition needs to employ AI and machine learning principles and deep neural network architecture to better support an increasingly diverse user base across a growing number of applications—all done in a network architecture at a comfortable pace.

This will enable businesses to offer fast, frictionless and intuitive voice experiences that offer the high level of flexibility that’s expected today.

Use cases continue to evolve

As it allows people to navigate customer experiences, find answers, seek help, access services, return products and more in the most natural way, voice tech is proliferating across nearly every industry. Below, we touch on just a few of many examples. 

• Customer intimacy analysis

  Retail businesses are using audio mining software to analyze call center conversations and better understand their customers as individuals, rather than grouping them into more generic ‘personas’. An ASR powered by AI and ML can accurately understand the streams of narrative and extract the most valuable customer insights from these conversations. Also, if the technology is well-attuned to dialects and accents, it will be able to create richer demographic profiles of the customer base. We are essentially entering an age when a business can go beyond knowing what customers are saying, to understanding who they are. 

• Consumer order placing

  Another application of speech recognition and transcription is in the consumer industry, where enterprises give customers a chance to order goods more quickly and intuitively. While it takes time for someone to scroll through a menu or go through a series of taps and swipes to find what they want, a speech-enabled solution allows a customer to simply voice their requests, including any special instructions, and place an order in seconds. This eliminates frustration and enhances customer satisfaction.

• Increasing use of virtual assistants

  Gartner predicts that, by 2023, 25% of employee interactions with applications will be via voice, up from under 3% in 2019. [2] Voice-enabled virtual assistants can be used to support an IT help desk team, for example, by interpreting incoming requests and performing routine tasks like resetting passwords, restoring services and so forth. Being able to ask for routine assistance quickly and naturally using voice, employees have more time for their critical work.

Combining capabilities for optimum value  

There are some AI and ML driven ASR platforms available that combine speech recognition and authentication capabilities (i.e., voice biometrics) to amplify the speed and effectiveness of voice-enabled services. This type of technology can understand what your users are saying as well as identify and authenticate who is speaking. This way, a company knows if a call involves a legitimate customer or not, without the need for multi-factor authentication or screening questions that involve live agents. The customer gets assistance faster, agents spend less time on routine authentication, and fraud is flagged more efficiently.

In another scenario, a retail company may want to use its bank of audio files to conduct analysis on a set of calls or exchanges between customers and agents. It could either be looking to understand more about its customers or measure its agents’ performance. Rather than having to look through the transcription of the call and manually separate out the customer’s narrative from the agent’s narrative, the company could use a combination of speech recognition and voice biometrics to zone in on the relevant parts of the transcription without the need for human intervention. This can save a substantial amount of time if the company is a national chain looking at hundreds or thousands of hours of audio files, for instance.

The bottom line

When an ASR engine uses advanced AI and machine learning to combine speech recognition and voice authentication capabilities, as well as cater to a diverse user base and a broad variety of communication channels, its applications are vast, and this can transform how multiple industries do business.

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