The more we rely on gadgets, the more passwords we need. And let us admit: it gets tedious. In fact, 60% of people walk away from buying something because the login process is too annoying. How can you make it less complicated for users without compromising security? The answer might be biometrics, like using fingerprints, facial features, or voice recognition.
The first two options are common today while using voice is catching up. The global voice recognition business is expected to reach 8.3 billion USD by 2031, increasing almost 20% yearly. But is voice identification software secure enough? Let us discuss the technology in detail: how it works, what it does, where it helps, and how to use it efficiently and safely.
Does Voice and Speech Recognition Software Mean the Same?
A voice recognition system breaks down a voice into many features, compares them to samples in a database, and finds a match. This way, a system can identify a person, meaning understand who is speaking or verify that a speaker is the person they claim to be.
Voice recognition is often confused with speech recognition, a technology that understands what is being said. In some cases, two terms are used interchangeably. But in this article, we are talking solely about identifying a speaker.
Voice recognition is used in many areas, from reaching app profiles with just the voice sound to identifying a person during customer service calls. Let us look at bright examples, starting with our own business case.
Voice Command Revolution: A Step-by-Step Guide to Developing Voice Recognition Apps
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Voice Verification Software in a Geosocial Networking App: CHI Software Case
Voice assistants help us quickly provide personalized experiences to our customers when they use gadgets to shop, call their loved ones, or listen to music. But first, a device must identify who is speaking.
That is why the CHI Software AI/ML team made a voice recognition and transcription feature an organic part of our new project.
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Our client wanted to make a new AI-powered app where people can share and find recommendations for different venues nearby, much like Yelp or Foursquare, but with a voice assistant. People often look for places while driving or on the move. So, the voice assistant was more than a feature, it was a necessity.
Our team developed a multifaceted solution. We included all the key features to simplify searching for places, events, or new friends:
A dynamic calendar suggests nearby events based on the user’s current time and location;
Topic channels tailored to a person’s interests;
An in-app chat for easy socializing;
A personal recommendations system suggesting venues and events;
A voice assistant recognizes users’ voices, provides easy voice navigation, and translates street names and signs for better convenience.
This blend of features resonated well with the audience and successfully boosted users’ engagement. As a result, the app has created a vibrant online hub for socializing enthusiasts.
You can explore more about the solution and its workflow in our detailed case study.
It is not only voices: check out our article about sound recognition software
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How to Apply Voice Recognition: 5 More Use Cases
Voice recognition is not just for voice assistants. You could utilize it in banking, healthcare, public services, and many more spheres that require a comfortable yet secure way to authenticate users.
Public Services
It is like an extra lock on the door, ensuring only the right people can access special areas or important info. For example, when you call Australia Services, they use voice identification software to confirm it is really you.
Call centers can use voice as a password, letting customers safely access their accounts, manage personal data, or do specific actions over the phone.
Banking and Financial Services
Voice recognition in banking and financial services lets users confirm money operations using their voice.
Biometric Authentication
In biometric authentication systems, a voice becomes a personal key for smartphones, laptops, or other devices, adding extra safety.
Healthcare Apps
Voice verification software helps ensure that the right person is accessing private health and medical information.
Check out what challenges are common for image recognition
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5 Tips from CHI Software Engineers: How to Build Trust in Voice Recognition Software
As voice recognition is widely used to protect sensitive information and private data, you should think two steps ahead during voice authentication system development. CHI Software AI/ML experts advise the following:
1. Use a System with Numerous Identifiers
Ensure your system recognizes a wide range of voice and speech features (we recommend at least a hundred) to accurately identify individuals.
2. Use Multi-Factor Authentication
Combine different methods of authentication for added security, especially for critical issues. For example, include something the user has (like a SIM card), knows (a secret word), and is (a voice).
3. Restrict the Number of Attempts
Many reported cases of voice recognition fraud required several attempts to fool a system. So, limit the number of tries. To ensure valid users can authenticate, always contact customers who faced several failures during the voice recognition process and guide them to other verification methods.
Consider developing a voice authentication system that randomly verifies a user throughout the conversation, not just at the start.
5. Enable Liveness Detection
Make certain your system can distinguish between a recording and a live voice. This includes detecting background noise, analyzing speech patterns, and potentially asking the user to respond to prompts in real time.
Conclusion
Voice recognition is a great way to check someone’s identity accurately, offering users an easy way to log in. While the technology is on the rise, you can make it your signature feature and outdo your competitors on the way to your customers’ hearts.
But here comes an age-old (and the most difficult) question: how should you start? The first step is simpler than you think – just contact an AI vendor. Actually, we have one in mind 🙂!
Voice recognition operates by analyzing a person's voice and breaking it down into distinct features. Developers create systems that compare these vocal features against a database of voice samples to find a match. This process enables the system to recognize a user’s unique voice pattern and verify their identity.
What is the difference between voice verification and identification software?
Voice verification software is designed for security purposes, confirming a claimed user identity by matching their voice against a pre-registered voiceprint. In contrast, voice identification software searches through multiple voiceprints to determine who is speaking, which can be used for broader applications like sorting through large databases in security or law enforcement.
What are the applications of voice recognition?
Voice recognition technology is applied in numerous fields for enhanced security and user experience, from unlocking devices to authenticating transactions in banking. It is also used for hands-free operations like in vehicles or smart homes, where ASR (automated speech recognition) system development plays a crucial role in understanding user commands.
Can voice impersonators or synthetic voices fool voice recognition systems?
While voice recognition software has significantly improved, it can still be challenged by advanced voice simulations. However, with robust voice authentication system development, features like liveness detection and analysis of speech patterns are employed to differentiate between genuine users and imposters or synthetic voices.
What is the difference between voice recognition and transcription?
Voice recognition software identifies who is speaking, while transcription involves converting spoken language into written text. Voice recognition and transcription may intersect in technologies such as ASR, but transcription is more focused on accurately documenting spoken words and does not typically involve identifying the speaker.
Alex is a Data Scientist & ML Engineer with an NLP specialization. He is passionate about AI-related technologies, fond of science, and participated in many international scientific conferences.
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