In this article, you’ll learn what speech recognition is, types, applications, and how does speech recognition work.
What is Speech Recognition?
Speech Recognition is also known as “Speech-to-text” when a machine or computer program identifies a human’s spoken words and converts them into text format. Speech Recognition technology enables various devices to understand the command through human spoken words and automatically translate it into text. In contrast, voice recognition is a biometric technology that identifies a specific person’s voice.
How Does Speech Recognition Work?
Speech Recognition technology is mainly used to convert a person’s spoken words into text for machine understanding.
Speech Recognition can be divided into three categories:
- Automatic speech recognition (ASR): It’s used to transcribed the audios
- Natural Language Processing (NLP): Deriving meaning from speech data and the subsequent transcribed text
- Text-to-Speech: The speech Recognition system converts text into human-like speech
Speech Recognition begins with digitizing a recorded speech with ASR and breaking the voice into segments of several tons in the form of spectrograms. Each spectrogram is analyzed and transcribed based on the NLP algorithm to predict the probability of words. Algorithms start to consider both a human’s spoken words and knowledge to understand the best possible command and analyze using TTS.
In simple words, the speech recognition software analyzes the person’s spoken words, breaks the speech into bits and converts it into a digital format for better understanding and then responds best possible based on similar patterns.
What are the Types of Speech Recognition?
Speech recognition can be Separated into different types of speech recognition. Mainly there are five types of speech recognition:
1. Speaker-dependent system
A speaker-dependent system must be developed specifically for a single speaker. This type of system is easier to develop, cheaper and more accurate to run more smoothly, accurately and efficiently. A computer must be pre-trained before the speaker’s voice is understood more effectively.
In this speaker-dependent system, the system is trained by repeating the vocabulary of words like pre-built templates. When a speaker-dependent system analyzes the human’s spoken words, it executes the command if it matches with the system.
2. Speaker independent system
It’s a speaker-independent system, so it executes commands based on analyzing the audio and converts into machine format to execute commands. There’s no prebuilt information saved in these independent systems because any human’s audio can be given to this system, and based on the algorithm, it performs the command.
Whenever audio is provided in a speaker-independent system, it’s converted into words for machine understanding and matched with related words to proceed with the command.
3. Discrete speech recognition
In discrete speech recognition, the speaker must pause between each word so that the speech recognition system can identify each word separately and execute accordingly.
4. Continuous speech recognition
This type of audio is a normal rate of speaking that speech recognition systems can easily understand.
5. Natural Language
Natural language is like humans communicating with computers, and computers recognize the words without using vocabulary. In the speech recognition system, NLU is used to understand the human spoken queries and answers the questions.
Application of Speech Recognition
Some of the Popular speech recognition digital assistance are:
- Amazon’s Alexa
- Apple’s Siri
- Google’s Google Assistant
- Microsoft’s Cortana
These are the most popular virtual assistants, and surely you’re familiar with their concept of voice command and communication with AI.
Speech Recognition has wider use cases in different sectors like banking, marketing, healthcare, IoT, customer services, etc. Let’s discuss this in detail.
The main aim of using a speech recognition system in banking is to handle the customer’s queries. It’s cost-effective and reduces the need for employee costs. It’s a virtual assistant that helps customers know about their banking details, balance, transactions and payments history by asking a voice assistant. Customers will get answers instantly to their queries and boost customers satisfaction and loyalty.
The demand for voice search is increasing rapidly, and businesses are starting to understand the data and find potential customers to stay ahead of the trend. So marketers should shift their focus to sharing auditory information with their customers for better results.
Healthcare is one of the important sectors where the demand for virtual assistants is higher.
Virtual assistants in the healthcare industry can be beneficial for:
- Quickly find the information from records
- Remind medicines, operations and other instructions to nurses and other staff
- Virtual assistants are useful for a consultation to know more details about common diseases and for guidance
- Work faster and less paperwork
4. Internet of Things (IoT)
You’ve noticed that Virtual assistants like Alexa, Siri, and Google Home are now connected with smart homes and control many devices like lighting, AC, TV etc., which can be easily accessible via voice command. This happens because of speech recognition technology used in IoT. And the growth of speech recognition technology is growing, and soon you’ll see cars connected with voice commands and many other inventions.
Voice biometrics is one of the safest security systems that use a specific person’s voice as a password to unlock. Some big places at this level of security need to use voice biometric technology.
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