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.
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.
Most people today—whether they are your customers or employees—want intuitive, frictionless experiences when they use your voice-driven solutions.
Automatic Speech Recognition (ASR) can help you save time, ramp up efficiency and scale services in your contact center and many other scenarios. However, not all ASR solutions can deliver experiences that measure up to customer expectations. Those that are too slow, too limited or riddled with inaccuracies can negatively impact purchasing decisions, erode customer trust, and discredit your brand.
It is therefore critical that you get speech automation right. And if you don’t, someone else will: Experts predict that voice commerce will grow to become an $80 billion industry by 2023.
That said, there are many hurdles in this advancing technology field. One of the key complexities is managing and expanding different dialects within a single language model. Even when two users speak the same first language, their dialects (US English vs. UK English, for example) must be managed as two completely separate languages by conventional ASR engines. These models must rely on dialect-specific lexicons and phonetic training, which can be resource-intensive, and quite expensive, especially if you serve a diverse and/or global audience.
To address this and numerous other issues, LumenVox has upgraded our ASR capabilities to bring you a new ASR engine with Transcription that is more accurate, efficient, and scalable—so you can maximize your return on investment.
Build world-class customer experiences with the LumenVox Automated Speech Recognition engine
At LumenVox, we have spent over two decades advancing our speech recognition capabilities through the development of our ASR engine. With state-of-the art transcription capabilities that are built on the foundations of Artificial Intelligence and Deep Neural Networks (DNN), LumenVox’s newly updated ASR engine offers several benefits that will help you offer seamless, accurate, super-scalable voice-enabled solutions.
With LumenVox, you can create user experiences that raise the bar—via your IVR (Interactive Voice Response), chatbot or virtual assistant.
Improved accuracy Our new ASR engine offers a low word error rate (compared with competitors), for exceptional reliability and accuracy. That means you can build high-performance applications time and time again.
Incredible scalability While other ASR engines on the market treat each dialect as a separate language, LumenVox supports multiple dialects with one language model. Take English as an example: where conventional solutions recognize US English and UK English as two separate languages, our ASR engine supports multiple dialects (including US, UK, Australian, South African and Indian) within one “universal” English language model. The same approach is true for Spanish (Castilian vs. Latin American), French (European vs. Canadian vs. Haitian), Portuguese (European vs. Brazilian), and other languages.
The ability to understand various dialects with one model means you can serve a diverse base of customers with a high level of accuracy, faster and for less cost than alternative ASR products. LumenVox uses an end-to-end DNN transcription process when building its language models so there is no need for a phonetic lexicon to understand various dialects for a given language. This speeds up the process of adding new models and when updating existing models with new language data. Your customers can also easily extend and adapt the default language models for their own needs, without having to deal with phonetic spellings and lexicons.
Greater efficiency With a high compression ratio of words and phrases known as ‘n-grams’, our ASR engine allows a greater number of word/phrase combinations to be simultaneously matched to the speaker conversation. This means that an accurate match can be identified in real time. As a result, users will experience little or no latency when interacting with your applications.
More flexibility With a selection of licensing and entitlement options to choose from, it is easy to deploy our ASR engine for any monetization model. Options include monthly subscription and usage based. This offers a refreshing alternative to competitor licensing models, which “lock” licensing entitlement to a single monetization framework.
Designed for simple implementation and management, LumenVox does not require costly professional services for delivery, installation, upgrades, or maintenance. Additionally, it fits in any network architecture, including premise-based installations, multi-cloud installations and hybrid prem/cloud installations.
Simplify the migration of legacy applications
LumenVox provides a streamlined way for customers to migrate off their incumbent ASR by simplifying the migration of grammars and confidence values to the new LumenVox ASR engine.
All these features work together to help you automate your customer and user experience in a more cost-efficient, reliable, and rewarding way.
Join Joe Hagan, chief product officer at LumenVox on August 24 at 11:00 a.m. PT / 2:00 p.m. ET as he discusses what is required to deliver meaningful employee and customer experiences through voice channels. Register now for the webinar.
Bots are fundamentally changing the way business and commerce work. Powered by Artificial Intelligence chatbots are now a critical part of digital strategy, automating customer interactions, with instant customer gratification. As voice technology becomes more popular and widely used, there are two chatbot options: text-based chatbots and voice-enabled chatbots. Which is the future?
What is a Chatbot?
A chatbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. What makes a chatbot powerful, is its ability to answer queries. Natural Language Processing (NLP) extracts intent from utterances. NLP is what makes chatbots intelligent. It’s the artificial intelligence (AI) which powers their ability to learn and provide the most relevant information to each unique query.
How Text-Based Chatbots Work
As the name suggests, a text-based chatbot is the one that interacts and communicates through text or messaging. Text-based chatbots are amazingly effective and efficient because they quickly sense the need of the users and provide them with immediate outcomes. In addition, chatbots assist in gathering feedback and keeping the customers engaged by solving queries quickly.
Text-based chatbots are extremely efficient when programed to accurately sense the need of the user and can provide immediate outcomes. In addition, chatbots also gather valuable feedback and ensure customers are engaged quickly. Text-based chatbots are primarily used to handle customer interactions and are often merged with messaging apps, social media, SMS and other channels.
But here’s the thing, chatbots are text-based which means they require typing, pressing, handling by the customer.
How Voice-Enabled Chatbots Are Changing the Game
A voice-enabled chatbot uses pre-recorded answers and text-to-speech to address customer queries. There are two ways to program these:
Users command the chatbots in either oral or written form, and then reply with their voice.
These “voicebots” are quite fluid, leveraging Conversational UI, or user interfaces based on human speech, written, or spoken. Instead of buttons, links or graphics, the customer uses spoken words to guide the conversation. What’s really neat is that as these bots continue to evolve, they can mix conversational UI with graphical UI; combining the intuitive nature of speech with the immediate gratification of graphics.
Voice-Enabled Chatbots vs. Text-Based Chatbots
The key to the decision between Voice-enabled chatbots vs. Text-based chatbots is to understand the preferences of your customers. According to a recent PricewaterhouseCoopers survey, approximately 71 percent of consumers prefer to use voice searches to conduct a query over the traditional method of typing. Recent reports estimate that 112 million people in the US will use a voice assistant at least monthly, up 10% from the previous year.
Driving Customer Engagement with Voice
Voice use is particularly relevant to people who are multitasking and need answers quickly, without having to put down what they’re doing and type or press a button. Voice-enabled chatbots are that next step forward for continuous, relevant customer engagement. They can be available 24/7 online and ready to serve anyone, anywhere, hands-free.
Give Your Chatbot a Voice
LumenVox Automated Speech Recognizer and NLU Gateway can give your chatbot a voice, boosting your CX strategy and increasing operational efficiency. To learn more, contact us here.