May 7, 2021
Stay in the Know
Speech Technologies encompass a broad scope of tools. Conversation Analytics is just one piece of the puzzle. Speech recognition – the ability for a computer to recognize spoken words – is another piece, and it has become pervasive in everyday life, both at home and at work. In fact, its growing use by consumers has nudged businesses into utilizing the technology. The Global speech recognition market is expected to see a CAGR of more than 20% through 2026. As a part of the conversation analytics mix, automated speech recognition (ASR) can help companies better understand their customers and improve the brand experience.
In this blog, we’ll look at how speech recognition has evolved since it was first introduced. Then, we’ll explore five ways in which businesses can benefit from using today’s automatic speech recognition (ASR) tools.
Background on Speech Recognition
Many think that speech recognition began with the advent of Siri from Apple in 2011. But its roots can actually be traced back to 1952, when Bell Laboratories introduced the Audrey system, which could recognize nine digits spoken aloud. A decade later, IBM Shoebox was introduced, with the ability to understand and respond to 16 words. Advancements really started to heat up in the 1970s when the US Department of Defense and Defense Advanced Research Projects Agency developed the Harpy speech system that could understand over 1000 words.
The personal computer propelled speech recognition in the 1990s, which is when BellSouth introduced its voice portal, VAL. This was the first time speech recognition was used in phone systems. By the early 2000s, speech recognition achieved close to 80% accuracy. Google Voice Search launched in 2009. It included 230 billion words. Apple launched Siri two years later. Since then, there has been a profusion of advancements in speech recognition.
Today, advances in machine learning, natural language processing, analytics, and AI-enabled technologies have fueled improvements in automatic speech recognition. Businesses in a variety of industries rely on ASR to direct callers to the right department or person through an IVR system. For contact centers, ASR is often the first point of interaction between a brand and its customers, and is fundamental to gaining consumer insights through conversation analytics. When used correctly. it can also raise the level of customer experience.
5 benefits of ASR
There are many business uses and benefits of ASR. Here are the top five.
1. Connecting customers to the right agent
Many companies, including contact centers, leverage ASR to provide advanced call routing. Both businesses and customers benefit. Customers are routed to the person with the best skills and ability to answer their questions or solve their problems. This avoids the hassle and frustration that customers endure when having to repeat their verification information multiple times. It also leads to faster resolution which provides a better overall customer experience.
2. Mitigate compliance issues and legal risk
ASR and custom rules that monitor legal compliance and script adherence enable companies to identify non-compliant language and interactions missing the necessary disclosures. This helps management train and coach to ultimately mitigate compliance risk. Customers are better served and businesses avoid fines and other legal action. This function can also be used with real-time analytics to automatically prompt an agent to include the compliance information that was missed, while the call is still in progress, to proactively reduce non-compliance incidents.
3. Enhance analytics, identify process gaps and identify trends
Conversation analytics leverages the unstructured data captured by ASR and then analyzes and extracts valuable insights so that businesses can better serve their customers and improve their operations. The better the speech recognition engine, the more accurate the analysis. The use of speech recognition technology with conversation analytics also allows quick identification of process gaps that can undermine a company’s customer experience. By spotting trends, speech analytics and ASR can identify best practices that can be shared with all agents.
4. Capture Sentiment
Speech recognition and natural language processing provides the ability to understand sentiment. Recognizing emotive tones in a customer’s or agent’s voice is essential to understanding the feeling behind the spoken words. For example, a customer may say, “thanks for the great support.” Analyzing the words alone, one might conclude that the customer had a satisfying experience. But, analyzing the vocal tones reveals that the actual sentiment was sarcasm and the true customer experience was negative.
5. Self-service 24/7
ASR can provide self-service options, creating additional capacity during peak times and after business hours. Customers calling to check on or change a delivery date, make an appointment, place a repeat order, get an account balance, make a payment, or update account information do not necessarily need to talk to a live agent. Speech recognition captures the request and powers virtual assistants to walk callers through the process.
Contact us for more information on how automatic speech recognition technology can help your business.
Webinar on Demand: SOLUTION SHOWCASE – Real-Time Service Improvements with Conversations Analytics Now
With real-time analysis and personalized coaching for live conversations, Noble Conversations Analytics Now helps you achieve faster response times and improved customer service. In this Solution Showcase, you will see how CA Now detects spoken phrases, sends agent and management alerts, and monitors empathy and active listening.
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Video: A Conversation with Jim Noble and Nancy Jamison
Frost & Sullivan Principal Analyst Nancy Jamison and Noble Systems CEO Jim Noble talk about the increasing interest in speech analytics as the technologies change and become more user-friendly.
Blog: The Evolving Capabilities of Conversation Analytics
Over the last ten years, call center speech analysis has been catapulted into a future we did not expect. In the beginning, the state of the art was represented by IVR phone trees which could barely understand the words “yes” or “no.” Now, one out of every four Americans has a conversational smart speaker. Technology has changed that fast. In this blog, we’ll explore the established capabilities of speech analytics, how it has evolved, and how conversations-based analytics expands on those features.