Biometric verification is evolving with technologies and developments in the world of AI. We have progressed significantly from basic fingerprint scanning to facial verification. Now, major developments in voice recognition enable us to authenticate even more conveniently. It is becoming accurate with the use of new technologies and efficient algorithms to eliminate the chances of error and mismatches. Businesses across different sectors are working with voice recognition systems, especially in the smartphone industry. According to research, the voice recognition market will touch new heights in 2025, with a projected reach of $8.5 billion. The annual growth rate (CAGR) of 13% shows a significant importance for voice recognition solutions in the upcoming years.
This article will explore voice recognition in greater depth, detailing its core principles, use cases, and emerging industry trends. So, let’s get started!
What is Voice Recognition?
Voice recognition is a biometric process that identifies the user by analyzing their voice characteristics. ASR (Automatic Speech Recognition) is another name for this operation. In this process, the system evaluates the audible traits of a voice such as frequency, pitch, flow, and accent. Voice recognition serves as a contactless and convenient biometric verification method that verifies the identity on the go. With new advancements, voice recognition is becoming more central to modern businesses as it saves time and effort while providing a high level of security. Pairing it with other biometric verification methods, such as facial recognition, improves security further. Some key examples of voice recognition applications are Google Assistant, Samsung’s Bixby, and Apple’s Siri voice-unlocking features. Although voice recognition software is becoming more mature with time, still some risks need to be eliminated with this technology to be used solely.
Difference between Speech and Voice Recognition
Both buzzwords are common in the tech market, and people often confuse them by using them interchangeably. Each technology serves a different purpose and is distinct. The difference between them lies in their use; for instance, in voice recognition, the main goal is to identify the type of voice rather than the words. Voice recognition is a deep learning solution that identifies a voice by analyzing its minute details, from pitch to all other audible characteristics. It performs mathematical calculations to uniquely differentiate between different voices. On the other hand, speech recognition, also referred to as speech-to-text, is a technology that focuses on words rather than the speaker. Its purpose is to convert spoken words from audio into an editable text format. Speech recognition is primarily used for transcribing audio to create a document for that audio content.
The History of Voice Recognition
The history of voice recognition dates back to the early 1950s when computers only understood a few syllables or digits. This technology got a boost in the past 5 decades, and now with AI, the use cases are limitless. Here is the timeline of voice recognition technology:
- 1950s: AUDREY, the digit recognizer system, was the first machine that could understand numbers in single digits i.e. From 0 to 9. Bell Laboratories built this automatic recognizer machine, and it was the first significant invention in the world of voice recognition.
- 1970s: The US Department of Defense jumped into the voice recognition world in the mid-1970s, with the development of a research project agency for speech understanding. It funded the projects that led to the production of HARPY, a voice recognition system that can now understand words. This system was built by Carnegie Mellon and was capable of understanding up to 1011 words.
- 1990s: Two significant milestones were achieved in 1990 with Dragon Dictate and IBM’s ViaVoice. Both systems are customer products that are capable of understanding continuous speech audio.
- 2011: In 2011, Apple made the landmark voice assistant, Siri. Later after which other companies such as Amazon and Google introduced their voice recognition systems.
How Voice Recognition Works?
With the smartphone industry utilizing voice recognition at great accuracy, other organizations are stepping into it with more data and information feeding to make it better. The voice recognition process works on neural networks and AI analysis. Generally, it consists of these core steps:
Analog to Digital Conversion
The first step of voice recognition software is to convert analog signals into digital computer-understandable codes. This process is also known as (A/D) conversion and enables the system to analyze the digital signals by comparing it to the word database or syllables.
Pattern Recognition
The second step is recognizing the voice patterns of the converted analog signals. The memory loads the existing patterns from the hard drive when the program is run. The comparator checks for these patterns to match with existing ones.
Voice Recognition Models: Markov & Neural Network
Voice recognition systems analyze the audio from one of the two models, the Markov and Neural Networks. Both models break down the words from the audio into their phonemes.
All these phases contribute to the voice recognition process. From conversion to pattern matching, each step involves clean identification of patterns.
Voice Recognition Algorithms
The voice recognition process performs multiple checks on the voice input through the microphone. Several algorithms are used for this process, but all of them serve only two purposes: audio training and testing sessions. The software solution manipulates the audio signal at different levels using various operations, such as Pre-emphasis, Windowing, MelCepstrum analysis, and framing.
Feature Extraction Algorithm (MFCC)
The extraction of voice data through acoustic signals involves different algorithms and steps. The MFCC operation mimics human hearing perceptions and is limited to frequencies over 1KHz. MFCC has a critical bandwidth range with the frequency of [8-10]. It has two filters to identify the features of the voice coming from the input. Here is the actual workflow of the MFCC process:
Feature Matching (DTW)
The second type of algorithm is feature matching, which uses dynamic programming principles. It evaluates the patterns and similarities between two series of voice times that can vary in speed, resulting in optimal alignment. The DTW principle relies on comparing two dynamic patterns to calculate the minimum distance.
AI and Voice Recognition
Like every other biometric identity verification system, AI is improving voice recognition as well. It simplifies the voice authentication process by enhancing the voice’s pattern-matching characteristics and phonetic traits. It helps the system register the individual’s voice data with proper evaluation at faster processing rates. Implementing machine learning algorithms allows more intelligent identification with unique element identification in every voice pattern. This further increases security and mitigates the risks of breaches within voice recognition technology. Organizations can use AI and voice recognition to enhance their customer onboarding process, especially when hiring remotely.
Key Features of AI Voice Recognition
- AI voice recognition is fast and works independently with access to large data sets
- With AI, the system understands the user input efficiently and marks correct nodes for identification
- AI enables voice recognition systems to incorporate biometric identification of multiple people at a time
- The interaction with voice recognition systems is becoming contactless, as well as AI enabling faster data transfer from input to output
- With Artificial intelligence, businesses can integrate voice recognition technology with different hardware devices
- Cloud databases and repositories allow safe interactions with complete backup and support
- ML also enables voice recognition systems to convert spoken words into written text quickly
- AI provides the foundation of contactless biometric verification
AI Accessibility and Risks
The easy access to AI tools and software for different purposes poses a significant risk to modern biometric systems. Especially when it comes to voice recognition! For instance, all the fake song covers/voiceovers on social media are the result of wide and prevalent access to AI voice recognition software. Businesses need a foolproof solution when using voice recognition systems, as fraudsters can bypass the entry point with ease by matching AI audio. Also, the method to recognize the audio through its characteristics needs refinement with the latest technology and tools to reduce the risk of breaches. Ultimately, the legal authorities need to regulate the use of AI software to limit its access for the betterment of people and legal entities.
Use Cases of Voice Recognition
Voice recognition has various use cases. It serves as a foundation for contactless biometric identification. Moreover, different industries use voice recognition for reasons other than security. Here are some use cases from a wide range of sectors that utilize technology.
Automotive
The automotive industry uses voice recognition technology for contactless and keyless vehicle entry. Next-generation cars use AI solutions to quickly identify the driver and unlock the door for them when they approach using smart sensors. Also, the built-in software system uses voice recognition to perform different functions inside the vehicle. For instance, you can turn on voice navigation with just your voice. Hands-free or autopilot mode is also a use-case scenario for voice recognition systems in the automotive sector. The implementation of these smart systems enables both personalization and security, while also maintaining convenience for the modern automotive sector.
Technology
The tech sector is the primary consumer of voice recognition technology. It embeds every new solution into our daily lives by incorporating it within our devices. For instance, the virtual assistants present in our mobile devices are a part of AI voice recognition systems. Google Assistant, Bixby, Alexa, and Siri are all trained on voice recognition algorithms to correctly identify the user’s voice and respond promptly. With more advancements in AI and IoT (Internet of Things), we will witness further improvements in the tech sector with more voice-based applications.
Healthcare
The healthcare sector uses voice recognition solutions to mitigate patient fraud risks. For instance, attackers try to impersonate a patient to get their legal insurance, here voice recognition biometrics can help mitigate this risk. With a contactless solution, healthcare institutes can verify the real patients. Moreover, voice recognition can help doctors and nurses leverage the use of contactless diagnosis. This enables them to operate the machinery on voice commands while working on other necessary patient protocols. Voice biometrics can also help in patient log management with dictation, reducing the treatment time that benefits everyone.
Sales
In sales, voice is everything. The clearer you convey or pitch your product, the higher the chances for an increase in sales. This department can leverage voice recognition by transcribing large numbers of phone calls into text. It can also help businesses to identify their real salesperson over a call by analyzing the voice input. Another use case in sales is letting the AI voice of the business representative take over and assist the people interested in the product. On legal grounds, it can be done with proper security and awareness protocols.
Security
Security is the primary use case for voice recognition solutions. As it serves as a biometric identification method. With proper implementation, it can help reduce online fraud and impersonation scams over the internet. Voice recognition can also work in parallel with other biometric solutions as a two-step verification layer. This further strengthens the security protocols of digital accounts and can ultimately reduce breaches and digital theft. Moreover, the voice recognition solution is contactless, which means all other health-related concerts are covered here as well. It is a great alternative in areas with limited physical interaction.
Conclusion
When applied appropriately, AI can revolutionize the field of voice recognition. Starting from the early 1950s, voice identification has come a long way. It can evaluate the voices correctly now and with the help of modern technologies such as AI and IoT, businesses are implementing real-life scenarios with this technology. The process has great future potential to serve as a biometric verification solution. However, some risks need to be addressed, especially with large-scale access to AI voice-replicating software. With proper regulation and compliance to fair-use, voice recognition solutions can enhance security, reduce patient treatment times, revolutionize the automotive sector, and increase business sales.
FAQs
What are some limitations of Voice Recognition solutions?
Voice recognition systems struggle with people who speak fast and use diverse accents. Microphone quality and system efficiency with internet dependence are also a few constraints of voice recognition.
What are the common uses of voice recognition?
It is present in our smartphones, laptops, and AI chatbots. Software that performs voice recognition biometric tests also incorporates this technology to verify its users.
How much is the accuracy of voice recognition in ideal conditions?
In ideal conditions, voice recognition can reach up to 95% accuracy.