Technology has always played a meaningful role in sales and customer service. Now—with advancements in AI and a rapid shift to digital during the pandemic—more call center leaders are embracing advanced solutions.
Their goals? To accelerate performance, bring down operating costs and deliver more value to customers.
To remain relevant in these circumstances, it’s vital for companies to equip their call center agents with the tools to perform at their best. One worthwhile addition to this toolkit is AI-driven auto dialer software.
The purpose of auto dialer software
When call center agents have to use manual dialing methods for their outbound campaigns, they can waste a lot of time punching in numbers, waiting for people or machines to pick up, and dealing with lines that are busy.
An auto dialer is a software solution that streamlines the outbound calling process by automating a range of tasks for your call center agents. These include auto-dialing numbers, optimally timing calls and delivering automated messages.
An intelligent auto dialer solution can save valuable time in your call center, which can be redirected towards other revenue-generating activities. However, not all auto-dialers are up to the task.
Where traditional auto dialer software falls short
Conventional auto dialer software has its limitations. As this type of technology uses only audio energy levels and timing cues to determine whether the call has been answered by a human or a machine, static on the line or background noises can easily confuse the software, with a negative impact on accuracy and timing.
When a human answers the call, the software may mistake it for an answering machine. Rather than connecting that person with an agent, the software would then deliver a pre-recorded message—which comes across as unprofessional and off-pitch. Alternatively, the call could be dropped just as the customer answers.
These types of determination errors frustrate your customers and hurt your business.
What’s the solution?
To solve these issues, LumenVox has developed Call Progress Analysis (CPA), which uses an AI-driven algorithm called Voice Activity Detection (VAD). This technology is more accurate than traditional answering machine detection software because it uses advanced speech recognition and machine learning capabilities to assess whether a call has connected to a human or an answering machine.
This means that your auto dialer can classify calls precisely and promptly—instantly connecting the call with a live agent or leaving a message, depending on whether there is a human or answering machine on the other end of the line.
By harnessing AI capabilities in this way, your call center can increase agent productivity and enhance the success of your high-volume outbound messaging and sales campaigns.
Why choose LumenVox’s AI-driven CPA software?
Reduce inbound calls: Make the most of every call and deliver your message to customers successfully, so they have fewer reasons to contact an agent or phone your call center for important updates.
Increase productivity Accelerate your outbound calling processes, minimize errors, and free up more time for your skilled and experienced agents to have live conversations with customers.
Improve the customer experience: Customers get the information they need, in full, when they need it, which means less friction and more satisfaction.
Integrate with ease: LumenVox CPA technology conforms to a set of industry standards, and it is therefore easy to integrate into your existing calling platform.
Want to learn more? Download our latest eBook below which will help you optimize your outbound strategy.
Voice has remained pervasive for business communications, and it is especially having an impact in this Age of Digital Transformation. However, voice poses major challenges for Contact Center and CX professionals to keep their voice-based resources up and running — not to mention managing to keep technology fresh and add new capabilities.
Too often they feel they are “damned if they do” when buying into the view that customer care is moving to chat and text. Or they feel “damned if they don’t” to keep the voice channel up to date with the latest and greatest AI-infused technologies.
Dramatic improvements in automatic speech recognition (ASR) and voice technologies have transformed the role of voice communication in the enterprise for customer and employee-facing applications.
Speech recognition has reached unprecedented levels of accuracy. Synthetic text-to-speech voices are often indistinguishable from humans. Voice biometrics detects both real and synthesized imposters reliably and at-scale.
We’re excited to join Dan Miller and Derek Top of Opus Research along with Joe Hagan, Chief Product Officer at LumenVox, on Tuesday, September 14th at 10am PT/1pm ET, for a lively discussion on how speech and voice technologies are shaping next-generation customer and employee experiences, including:
Accuracy – how accuracy and other performance gains instill the confidence businesses need to build new voice-first applications
Accessibility – guidance on choosing the right technology foundation and partner to meet current and future business needs
Affordability – the myth of “it’s expensive” and why it no longer applies – and options for businesses where the reverse is true
Flexibility – Deploy speech applications in any environment, in any cloud: on-premise, multi-cloud, or a hybrid model.
“New demands have redefined the very meaning of Automated Speech Recognition,” said Dan Miller, lead analyst at Opus Research. “LumenVox’s new ASR engine provides high levels of accuracy and intelligence required to capture, recognize, and react to each customer’s intent and define what’s possible for speech and voice recognition software.”
Register now to save your seat! Can’t make it? Register to receive a link to the webinar recording!
Opus Research is a diversified advisory and analysis firm providing critical insight on software and services that support multimodal customer care. Opus Research is focused on “Conversational Commerce,” the merging of intelligent assistant technologies, conversational intelligence, intelligent authentication, enterprise collaboration and digital commerce.
With companies rapidly evolving and seeking more voice-enabled applications to deliver powerful experiences, LumenVox was pleased to recently discuss the benefits organizations can see when utilizing an Automatic Speech Recognition (ASR) engine with extremely accurate transcription, flexibility, and high availability.
The Power of Speech
ASR’s everyday applications are vast, and it’s transforming how multiple industries do business. For example, media and entertainment companies can produce content faster when hours of audio or video files are converted into searchable transcripts.
Educational institutions can deliver accessible remote learning through real-time captioning in video conferencing software. In addition, researchers can begin analyzing qualitative data in a matter of minutes thanks to asynchronous, machine-generated transcription.
These are just a few examples of how speech-to-text technology is impacting society.
In addition to industry-leading accuracy and speed, LumenVox’s ASR engine utilizes an end-to-end Deep Neural Network (DNN) architecture to accelerate the ability to add new languages and to recognize non-native speaking accents. This enables LumenVox customers to serve a more diverse base of users.
The Value of Artificial Intelligence
With typical Machine Learning (ML) models, there are two fundamental elements: (1) the language model and (2) the creator of the language model.
The language model can ‘learn’ based upon the data it’s given. With a DNN, creators are not required to augment the code base when building or adding data, which is helpful in eliminating inherent biases.
Ultimately, the more robust data sets will provide a highly accurate, broadly applied language mode.
Delivering Enhanced Customer Experiences with Speech
ASR is a programmatic way to turn voice into text. Voices come in different dialects, languages, and with various levels of background noise.
A good ASR can turn the spoken word from a variety of languages and accents into readable, understandable text. Businesses can then use the text to strengthen decision-making and enhance customer experiences by serving a more diverse user base.
Ready to learn more about automatic speech recognition? Join Dan Miller, lead analyst at Opus Research, and Joe Hagan, chief product officer at LumenVox on September 14 at 11:00 a.m. PT / 2:00 p.m. ET as they discuss what is required to deliver meaningful employee and customer experiences through voice channels. Register now for the webinar.
As an organization that interacts with customers through speech applications, the quality of your speech recognition technology can make or break your CX.
In an ideal world, communicating with technology via speech would be as easy and natural as conversing with a human. This would make it so simple to access information and services remotely. It would also offer more independence to those who have no other option but voice user interfaces, such as young children who aren’t literate yet and people living with visual, motor or mobility impairments.
While some speech recognition technologies have made great strides in achieving these ideals, others are still falling far below expectations. This raises the question, why do some speech recognition technologies work well, while others fail?
The reality is: human speech is complex and constantly changing.
The challenges faced by modern speech recognition tools
An Automatic Speech Recognition (ASR) engine’s job is to take speech and identify it as something meaningful. Some ASRs have transcription capabilities, which allow them to turn that meaning into something useful, like text.
Getting this right is actually an incredibly challenging process. Firstly, ASRs must keep pace with the fact that language is constantly changing. In 2021, for instance, Merriam-Webster added 520 new words and definitions to its American English dictionary.
Also, ASRs must be able to separate speech from background and environmental noise. This could be the sound of traffic, a busy shopping mall, or even the interference that occurs due to the quality of the microphone used.
Unfortunately, many ASRs are simply not capable of handling these variables efficiently.
How to solve these problems
All this considered, companies need to choose their ASR engines carefully when building or modernizing speech-enabled customer experiences.
There are many different types of ASR engines on the market. Ideally, you want one that:
Supports all dialects within a given language
Offers advanced artificial intelligence and machine learning capabilities for maximum accuracy
Is able to continually learn from real-world usage and expand the language model to serve a more diverse base of users
LumenVox ASR with Transcription: Next-generation speech recognition
Status-quo speech recognition engines don’t have the machine learning capabilities to manage all the differentials in natural human speech—certainly not with the accuracy users expect. This is where LumenVox’s new ASR engine changes the game.
The technology that sets the LumenVox ASR engine apart is its end-to-end Deep Neural Network (DNN) architecture and state-of-the-art natural language processing and understanding capabilities. This creates an ASR engine that serves a much more diverse base of users.
Whereas other ASR engines treat different dialects as separate languages, LumenVox’s new ASR Engine with Transcription supports multiple dialects with one language model. This considers many different pronunciations in a single language, as opposed to having to train according to each individual user. The end-to-end recognizer matches audio to the written word—regardless of accent or other factors that impact pronunciation.
Additionally, no matter where the call or audio is coming from, the LumenVox Speech Recognizer separates speech from background noise using Voice Activity Detection (VAD). This takes a range of qualities into consideration, including energy level (volume), frequency (pitch) and changes in duration, to accurately detect the actual speech.
All this means that your speech solution can cater for a more diverse user base, in a broader range of scenarios, with market-leading accuracy.
Improve your speech application success rate with tuning
To get maximum value from your speech applications, LumenVox also offers an advanced turning tool that does all the heavy lifting for you, making it far easier for you to manage tuning in-house (and avoid expensive professional service fees).
LumenVox’s Speech Tuner performs transcriptions, instant parameter and grammar-tuning, and version upgrade-testing of any speech application, in less time and with less effort. This way, you can continually enhance speech recognition accuracy and build competitive advantage.
While there is room for improvement in the speech recognition technology landscape, the demand for voice-enabled solutions continues to grow. A study by National Public Media found that 52% of voice-assistant users say they use voice tech several times a day or nearly every day, compared to 46% before the pandemic.
If your company gets speech recognition right, you will be in a strong position to capitalize on this market growth.
With smart speakers and virtual assistants like Amazon Alexa, Apple’s Siri and Google Assistant part of our everyday lives, most of us understand the concept of voice-enabled technology. But how does speech recognition fit into this landscape and, more importantly, what value can it offer your business?
What is Speech Recognition?
The goal of speech recognition is to let people operate applications and devices, and access services, in a more natural and convenient way—using voice. This reduces reliance on clicking, tapping and typing. These manual approaches are not only more laborious but also exclude certain customers, such as those with motor disabilities who can’t use keyboards or other tactile devices.
The brain behind the modern speech recognition system is called an automatic speech recognizer (ASR) engine. This intelligent software is able to interpret spoken audio and convert it from a verbal format into a text format. This text then acts as a command to drive the next steps of your speech-enabled solution.
Decades of Development
Speech recognition technology is by no means a new concept, but it has evolved substantially since the mid-20th Century. While today, you can carry voice-enabled technology in your pocket, the first documented speech recognizer, launched in 1952, involved an entire room of electronics. Made by Bell Labs, this ‘Automatic Digit Recognition Machine’ was dubbed Audrey, and it could recognize the sound of spoken digits (zero through nine) when it was ‘adapted’ to the speaker—a ground-breaking achievement at the time.
In 2021, there are a great many speech recognition applications and devices available on the market. The more advanced ASRs, built on the foundations of artificial intelligence and deep neural networks, are able to recognize a diverse range of natural languages and dialects, spoken by millions of customers, with great accuracy. All this translates into a high-quality, friction-free automated user experience.
But the journey is far from over. Speech recognition is an ever-advancing field and the market for this technology continues to expand. Looking forward, experts predict that the global voice and speech recognition market will grow at a CAGR of 19.5% during 2021-2026.
Looking at it from another angle: in 2020, there were over 4 billion digital voice assistants being used around the world. In just four years, that number is expected to double. That means there could be more voice assistants on our planet than humans in the near future.
Improve efficiency: Organizations can use speech recognition to step up productivity and performance through a wide range of services, such as voice-activated banking or apps that allow users to compose messages verbally.
Enhance your IVR: With a well-chosen ASR, you can boost accuracy and speed within your IVR, reducing agent handling times and routing calls more efficiently to improve the overall customer experience.
Support analytics: You can automatically transcribe all verbal conversations in your contact center. This makes these interactions easier to analyze, whether you’re using automated sentiment analysis tools to gauge customer satisfaction levels or flagging common call patterns and issues for swift resolution.
Enable multi-tasking: Speech-enabled applications are hands-free. This way, your users can do other tasks (such as drive) while accessing your service. This improves usability and customer satisfaction.
Scale your reach: As with any automated technology, you can scale speech recognition rapidly without increasing human headcount. This makes it easier for you to expand into new markets or manage seasonal spikes in demand.
When you think about it, there are so many ways for your organization to integrate speech recognition into your solutions and services, to boost usability, save time and enhance CX.
LumenVox Automated Speech Recognizer – Speech Recognition, But Better
To harness these advantages and meet customer expectations, it’s vital that you choose a high-performing speech recognition engine. LumenVox’s new AI-driven ASR engine is unique in its ability to accurately recognize naturally spoken language and learn from real-world use for maximum ROI.
To explore what LumenVox can do for your business, request a demo.
In this video, we explore the basic types of ASR, providing a technical overview and looking at the fundamental inputs. We also explain the difference between speaker dependent speech recognition software and speaker independent speech recognition software.
Speech Recognition 101 – Part 2
Part two takes an in-depth look at the grammar component of speech recognition. The number one problem developers have is building good grammars, or modeling how users speak to applications. Find out how to overcome these hurdles with LumenVox.