The Voice Recognition Market was valued at $10.7 billion in 2020 and is expected to reach $27.16 billion by 2026. The demand for voice recognition applications is growing in retail, banking, connected devices, smart home, healthcare, and automobile sectors. The number one reason for such growth is the demand for speech-based biometrics for identification purposes.
Voice identification passwords are more secure than traditional passwords and are being applied in banking and healthcare and coming to other sectors. Keep reading our guide to learn about the trends in speech recognition apps and tips on developing one.
How to Create Voice Recognition Software
So, how does speech recognition work?
“The lexical models are built by stringing together acoustic models, the language model is built by stringing together word models, and it all gets compiled into one enormous representation of spoken English, let’s say, and that becomes the model that gets learned from data, and that recognizes or searches when some acoustics come in and it needs to find out what’s my best guess at what just got said.”
— Mike Cohen, Manager of Speech Technologies at Google
Before proceeding to voice recognition software development, decide on the approach you’ll take. There are two main types of voice recognition applications:
Speaker-dependent apps are based on templates and can recognize the voice of only one person. The user trains the software in its voice by repeating certain sounds and phrases integrated into the “template.” Then, based on the templates, the program recognizes these sounds.
The second type—speaker-independent applications—can recognize the voice of multiple persons and do not require prior training. Such systems identify different accents, pitches, volumes, and speed with linear predictive coding (LPC) or Fourier transformations.
Before developing the technology for your app, you need to define:
- The business problem you want to solve.
- The features you need to implement first.
- What you are going to automate and what AI capabilities you need.
- A plan for software development and the methodology to apply.
- Technical capabilities you will use.
Keep in mind your end-users and their needs to create a personalized experience.
This blog post in Forbes, Comparing Google's AI Speech Recognition To Human Captioning For Television News, gives a great understanding of how APIs work.
If you want something more customizable – say, for an android voice recognition app, you can choose a library that contains the essential components for your app development.
Voice Recognition Apps on Different Devices
When it comes to devices for voice recognition apps, there are two deployment models you can choose from – cloud and embedded. Choose cloud if you would like to work on speech-to-speech conversations and voice recognition.
All these processes will be integrated into the cloud, and you will avoid overloading space on your device. Keep in mind that your Internet connection must be flawless for the cloud app.
The embedded model is located on your device, so you can use it offline. Also, your app will not suffer from any delays as you do not depend on a server. However, the embedded model requires a lot of free space on your phone or tablet because all the audio elements must be located on your device.
“Custom software development solutions can be an effective tool for developing voice recognition apps. With the latest innovations, the process of development can be simplified and customized to your needs. Voice recognition is rapidly evolving, and there are a lot of ways to make it work for your industry”
— Vlad Medvedovsky CEO at Proxet, a custom software development solutions company.
Creating Simple App for Voice Recognition: Challenges to Keep in Mind
Inaccuracy in Automatic Speech Recognition (ASR) Systems
Highly sensitive voice recognition applications can suffer from the reduced accuracy level because of surrounding noises. This lack of fidelity is a key challenge.
Lack of Efficient IT Infrastructure
A lack of knowledge or ability to implement new technologies can slow down or restrain the growth of companies or whole industries.
Lack of Trust
According to PwC, one out of four consumers say they would never shop with a voice assistant. And 46% surveyed said they don't trust their voice assistant to process orders correctly. So, if you want to gain widespread adoption of your voice recognition app, you must address these concerns.
Voice Recognition Stack
Let’s discuss the main levels of voice recognition systems.
Here is a short description of these technologies:
- MEMS microphones – a technology that helps to capture high-quality and clear voice
- Microphone array algorithms solve two major problems: environmental noise and reverberations.
- Automatic Speech Recognition (ASR) or Speech-To-Text (STT) takes a raw audio data stream and produces a text record.
- Natural Language Understanding (NLU) – in this case, the NLU system receives a text as input and gives back the human's intent.
- Skills Routing / Skill Execution / Cloud orchestration takes the “intent” with all extracted entities and executes the “business logic.”
- Natural Language Generation (NLG) receives structured data (like JSON, XML,...) and returns human-readable text.
- Text-To-Speech (TTS) or Speech synthesis – here, the last layer receives a text as input and transforms it into an audio signal played through a speaker.
To learn more about how these technologies are used, read the following guide on LinkedIn.
Mobile Apps: How to Build a Voice Recognition App with Different Technologies
If you want to create speech recognition with python, follow these tutorials:
Examples of AI/ML Speech Recognition Apps
Let’s see the most popular speech recognition apps on the market and their main features.
Dragon Anywhere is dictation software developed by Nuance for iOS devices. It can be used for dictating and editing documents of any length.
Google Cloud Speech API is used for processing real-time streaming and pre-recorded audio. It automatically transcribes the correct nouns, dates, and phone numbers.
The virtual assistant for Apple devices supports 21 languages and helps you find the answer to most of your questions and plan your day.
Amazon Lex is used for building a conversational interface. The developed bot can be used in the Chat platform, IoT devices, and mobile clients.
As you can see, the number of digital health applications is growing. Healthcare providers should follow the industry trends and take steps to improve the processes in their organization.
“While voice search isn’t perfected as of now, updates and advancements in voice recognition could get voice search based smart devices to a point where it is a better user experience than it is now and begin to be used more often.”
— Nicole Ramirez
Proxet is already able to provide software for voice recognition. With years of experience from experts and developers in the field, we will provide the best solution to transform your business and your industry.
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