Speech technology is foundational for most forms of communications applications. While the technology has been around for decades, much of it remains poorly understood. Pre-internet forms of speech recognition technology were built mainly around transcription, where the objective was to convert human speech into text as quickly and accurately as possible.
Analog forms of speech technology were manual and labor-intensive, but over time, this was replaced by speech recognition software. While this may have been more efficient, it wasn't necessarily more accurate, leaving speech technology in a stalled state until newer forms of technology came along.
The biggest leap forward in this space was the emergence of AI, especially in the last few years. AI encompasses many technologies, with the most relevant here being machine learning and natural language processing.
These technologies can process data faster and more accurately than humans, which not only improves transcription accuracy, but also opens up new possibilities that go beyond just transcribing speech to text. Perhaps the best-known example of this is automatic speech recognition (ASR), which we all use when interacting with virtual assistants like Amazon Alexa or doing voice-based search on smartphones.
AI-based technologies are both new and complex, which contributes to the limited understanding of what speech technology brings to IT decision-makers. Compounding this is the way that speech recognition is used so interchangeably with voice recognition. These two terms may seem to mean the same thing, but they are, in fact, different. Let's examine the difference between speech and voice recognition.
What is speech recognition?
With recent advances in both AI and cloud technologies, speech recognition has moved beyond conventional transcription, essentially becoming another data stream when converted into a digital format. This is where ASR provides rich business value for both collaboration and contact center applications. Now, speech can be used to replace text-based commands with voice commands, such as when dictating email or initiating a conference call.
Building on ASR capabilities provides a variety of speech-to-text applications that can both help workers be more productive and enable agents to engage more effectively with customers. Notable examples of these apps are automated transcription and real-time translation.
Examples of speech recognition
Speech recognition technology has become accurate enough now that workers don't need to take notes during meetings, as all conversations can be transcribed for review later. Dozens of languages are now supported by speech recognition software for translation, making it much easier for global teams to collaborate and for agents to communicate with customers anywhere in the world.
Taking things a step further are developments in conversational AI that enable chatbots to have two-way dialogue with humans -- even dealing with open-ended questions. Advances in natural language understanding make this possible, as AI can quickly recognize speech patterns and enable chatbots to perform more complex tasks that automate workflows and self-service for customers.
What is voice recognition?
In a narrow sense, speech recognition as outlined above could be referred to as voice recognition, and it is perfectly acceptable to do so, as long as the underlying meaning is clearly understood. There is, however, a critical distinction to be made. Whereas speech recognition pertains to the content of what is being said, voice recognition focuses on properly identifying the speaker and attributing each instance of speech to the correct speaker. Another way to distinguish between them is to remember that speech recognition is about what is being said, while voice recognition is about who is saying it.
Examples of voice recognition
On the collaboration front, voice recognition software is invaluable for conferencing, as there will often be multiple people speaking at the same time. Whether the use case is captioning so remote attendees can follow who is saying what in real time or the use case is the creation of meeting transcripts to be reviewed later, accurate voice and speaker recognition is now a must-have for unified communications.
Another key use case for voice recognition technology is validating a speaker's identity. Human speech can now be used to create voiceprints that are unique to each person, providing a rapid, touchless form of authentication. Instead of entering a password on a PC or keypad, workers can use their voice to join a conference call, access computer programs or restricted files, or gain entry to a facility or controlled space.
A more advanced example would be voice biometrics, which draws from AI analytics to go beyond validating identity. By analyzing speech patterns, voice biometrics can detect anomalies in real time to mitigate cybersecurity risks that come with digital technologies. In the workplace, this could protect against bad actors impersonating workers or executives to disrupt operations, access sensitive information or divert revenues. Equally important is the contact center, where voice biometrics are used for fraud prevention when a customer's identity is stolen and bad actors try to use the stolen identity when speaking to agents.
Dig Deeper on UC strategy
Related Q&A from Jon Arnold
UC offerings differ depending on business goals and intended users. When evaluating tools for frontline workers, consider factors based on ... Continue Reading
Employees are now signing into meetings from a variety of locations. What's the best way to support hybrid meetings and to ensure that workers remain... Continue Reading
Even in a hybrid work environment, the water cooler remains a valuable way for remote employees to communicate and stay engaged -- thanks to UC. Continue Reading