How to Make a Speech Recognition System
Wondering how to make your own speech recognition system?
This is a rapidly growing market that still has massive room for growth.
According to a study done by Markets and Markets, “The overall speech and voice recognition market is expected to reach USD 21.5 billion by 2024 from USD 7.5 billion in 2018, at a CAGR of 19.18%.”.
Besides all the success, innovating in this industry is the chance to make a positive impact on our global society. Here’re a few amazing case studies of companies who hired DevTeam.Space to build similar products:
- Face, Sex, Age, Recognition System – Machine Learning Program
- Air Sign – Machine Learning Program For Air Signature Recognition
- Neural Network Library – Machine Learning Neural Networks
Speech and Computers
As humans, we communicate with each other through speech quickly and easily. The thought of having to write down or type every sentence and thought in a casual conversation seems slow in comparison.
So why do we communicate with computers this way? Well, computers have been able to understand speech for a long time. But, they haven’t been that great at it. Until recently, speech recognition systems had topped out at about 80% accuracy. That’s OK, but correcting errors in 20% of the words you say gets annoying very quickly.
That’s all changing now. Modern speech recognition systems can now understand speech extremely accurately, and they even talk back to you in a way you can understand. Consequently, voice recognition development is a big industry.
So What is a Speech Recognition System?
Simply put, it’s any system that takes in audio and attempts to recognize and understand speech within it. These days, mobile looks like it will be the platform that voice recognition systems work best on.
Here’s a list of some mobile apps that use speech recognition:
- Google Mobile Apps – on Android, BlackBerry, and iOS – Free
- Bing – on Android and iOS – Free
- Siri Assistant – for iOS – Free
- DriveSafe.ly – for Android, BlackBerry, iOS – $13.95 per year
- Dragon Downloadable Apps – on Android, BlackBerry, iOS
- Jibbigo Voice Translation – on Android, iOS – Free
Today I’ll be looking at the tools these apps use to implement their speech functionality.
Why is Recognizing Speech So Difficult?
Like many problems in computer science, recognizing speech is more difficult than it seems. Something that seems trivial to you can take decades of research to automate with software.
Some of the factors that make it so difficult are
- The complexity of spoken language – In English, many words have multiple meanings depending on the context – for example “red” and “read” sound exactly the same but have completely different meanings.
- People talk fast – When we speak, we don’t break our sentences up into individual words – we kind of just blurt it all out in one long string of sounds with few breaks. This makes it difficult to determine where a word ends and the next one begins.
- No two people speak in the same way – It’s no good to have a system that needs to be reprogrammed for every individual. A system needs to be able to hear a new voice and understand it immediately.
- Background noise – Differentiating the speech from the background noise is very difficult. This is especially true if the background noise is also speech (say at a party).
How Does Voice Recognition Work?
Many institutions, scientists, researchers, and companies have invested in speech recognition research. As a result, there are a few different approaches that work to varying degrees. The main four methods are:
- Simple audio pattern matching
- More complex pattern and feature analysis
- Statistical analysis and modeling
- Artificial neural networks
1. Simple Word Pattern Matching
This method is the simplest way to build a voice to text converter, and it works quite well in some limited cases. It involves recognizing whole words based on their audio signature. You’ve probably used one of these systems before. When you call up a company and a machine asks you for your name or number, they are probably using this type of speech recognition.
The first thing a speech recognition system needs to do is convert the audio signal into a form a computer can understand. This is usually a spectrogram. It’s a three-dimensional graph displaying time on the x-axis, frequency on the y-axis, and intensity is represented as color. Here’s an example of a spectrogram of some human speech.
A pattern matching system will have a limited number of saved words it can understand. It knows what the spectrogram graph of each of these words looks like, and uses it to determine which word you said. This works well with very small vocabularies such as the number 0-9, but not much more.
2. Pattern and Feature Analysis
Technically, you could extend the above system to work with all the words. However, a typical person has a vocabulary of tens of thousands of words, so this would be a hugely inefficient way of doing things.
A better way is to learn the building blocks that makeup words and listen for those. You can then put these together to build and understand whole words and sentences. This is how the feature analysis works.
In reality, this still isn’t very accurate. Just because a computer can understand the sounds that makeup words, it doesn’t mean it can understand what you are saying.
3. Statistical Analysis and Modelling
A system that can listen to you speak and understand what words you are saying will need some understanding of how a language works. This is called a language model.
By mathematically analyzing a language, you can find patterns. Some words are very likely to be followed by other words, and others are rarely spoken in the same sentence. A phrase like “opened the…” is likely to be followed by words like “door”. If your software has access to a statistical model containing all this data, it can make much better guesses about what words were said in an audio clip.
This is the method most of the successful speech recognition systems have used for the past few decades. But, these methods reached a limit with their accuracy. Obtaining very high accuracy requires a more advanced voice recognition technology.
4. Artificial Neural Networks (ANNs)
Artificial neural networks are an attempt to get computers to work more like the human brain. Your brain doesn’t store specific encoded instructions, it has vast networks of neurons. These alter their connections to each other as new information past through them.
Speech recognition using this machine learning is taking off at companies like Google and Microsoft, which have huge databases of information to train these networks.
How to create voice recognition software?
The best way for voice recognition app development depends on your resources and what you want to achieve. Coding everything and building speech recognition from scratch isn’t required, as there are so many great tools and libraries available. Let’s take a look at some of the tools you can use to build your own speech recognition system.
Many of the big cloud providers have APIs you can use for voice recognition. All you need to do is query the API with audio in your code, and it will return the text. Some of the main ones include:
- Google Speech API
- Microsoft Cognitive Services – Bing Speech API
- Amazon Alexa Voice Service
- Facebook’s Wit.ai
This is an easy and powerful method, as you’ll essentially have access to all the resources and speech recognition algorithms of these big companies.
Of course, the downside is that most of them aren’t free. And, you can’t customize them very much, as all the processing is done on a remote server. For a free, custom voice recognition system, you’ll need to use a different set of tools.
Open Source Voice Recognition Libraries
To build your custom solution, there are some really great libraries you can use. They are fast, accurate, and free. Here are some of the best available – I’ve chosen a few that use different techniques and programming languages.
CMU Sphinx is a group of recognition systems developed at Carnegie Mellon University – each designed for different purposes. It is written in Java, but there are bindings for many languages. This means you can use the libraries and voice recognition methods even if you want to program in C# or Python. There are some great components you need to develop a voice recognition system.
For an awesome example of an application built using CMU Sphinx, check out the Jasper Project on GitHub.
Kaldi, released in 2011 is a relatively new toolkit that’s gained a reputation for being easy to use. It uses the C++ programming language.
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HTK, also called the Hidden Markov Model Toolkit, is made for the statistical analysis modeling techniques we discussed earlier. It’s owned by Microsoft, but they are happy for you to use and change the source code. It uses the C programming language.
Where to Get Started
If you’re new to building this kind of system, I would go with something based on Python that uses the CMU Sphinx library. Check out this quick tutorial that sets up a very basic system in just 29 lines of Python code.
Finding Developers That Can Help
Needless to say, speech recognition programming is an art form, and putting all this together is a heck of a job. To create something that really works, you’ll need to be a pro yourself or get some professional help. Software teams at DevTeamSpace build these kinds of systems all the time and can certainly help you get your app to understand your users very quickly. Learn how to build an agile development team and why it’s important for the success of your app.
Key considerations while implementing the speech recognition technology
Keep the following key questions and considerations in mind when you create and implement speech recognition software:
1. Define your business problems or opportunities to find the right use case
By now, you know that building a speech recognition system involves complexities. You need to first analyze your business problems and opportunities. Assess whether you have a viable use case for using the speech recognition technology.
Speech recognition technology has given rise to applications facilitating voice searches. Digital assistants like Apple’s Siri accept voice commands from users and respond to their requests. Many sectors like healthcare, government, etc. have high-value use cases involving this promising technology, and your organization might have one too. Identify the right use case.
2. Decide the functionality and features to offer
A user of an Apple iPhone has certain specific needs when using Apple’s Siri. Similarly, Google Home and other popular automatic speech recognition software deliver tangible value to users. These organizations undertook large scale studies to determine the scope of their “Artificial Intelligence” (AI) projects.
They often pushed the boundary and offered very helpful features. E.g., “Apple Dictation” is a useful speech-to-text app for Apple devices. Another example is the “Voice Access” app from Google. It helps users to make phone calls in hands-free mode.
You need to study your business requirements carefully. Subsequently, you need to decide the functionality and features to offer. Plan to support all key operating systems.
3. Plan the project meticulously
Plan meticulously so that you prepare sufficiently for the entire AI development lifecycle. Do the following:
- Define why you would use AI and what you will automate.
- Identify relevant data sources and gather large enough datasets.
- Determine the AI capabilities you need, e.g., “Deep Learning” (DL), “Natural Language Processing” (NLP), speech recognition, etc.
- Evaluate popular SDLC methodologies like Agile and choose a suitable methodology.
- Plan the relevant phases like requirements analysis, design, development, testing, deployment, and maintenance.
4. Decide the technical capabilities you will use, e.g., “Speech-to-text”
Depending on your business requirements, you need to choose one or more technical capabilities within the large landscape of AI. E.g., you might need to explore the following:
- “Machine Learning” (ML);
- “Deep Learning” (DL);
- Acoustic modeling for speech recognition;
- Generating optimal word sequences using “Automatic Speech Recognition” (ASR) systems;
- Using acoustic modeling for recognizing phonemes, which could help with speech recognition;
- “Hidden Markov Model” (HMM) decomposition, which helps to recognize speeches where there’s interference from another background speaker;
- Using continuous speech recognition;
- “Limited vocabulary” speech recognition techniques;
- Measuring speech recognition accuracy by using the “Word Error Rate” (WER);
5. Developing capabilities vs using 3rd party APIs
You will likely design and develop software to suit your requirements. For this, you will likely code algorithms and modules using Python. There are very good tutorials to create speech recognition software using Python, which will help.
In some scenarios, you might want to use market-leading APIs. This could save some time since you won’t reinvent the wheel. The following are a few examples of such APIs:
- The “Speech-to-text” API from Google Cloud: This API helps you to transcribe your content in real-time;
- The Automatic Speech Recognition (ASR) system from Nuance: Nuance offers an ASR system, which is especially helpful for customer self-service applications;
- IBM Watson “Speech to text” API: You can use to add capabilities to transcribe speeches;
- “Speech Recognizers” like CMU Sphinx “Recognizer”.
Speech recognition tech is finally good enough to be useful. Pair that with the rise of mobile devices (and their annoyingly small keyboards), and it’s easy to see it taking off in a big way. To keep up with your competition and make your customers happy, why not learn how to make a voice recognition program and implement it into your products?
Here are a few articles that might also interest you:
Frequently Asked Questions
What is a speech recognition system?
It is a software system that is able to recognize what people are saying to it. Speech recognition systems vary from simple voice yes or no and number recognition for customer care lines to sophisticated machine learning programs such as SIRI.
How does a speech recognition system work?
The process is simple. As the machine listens to the human voice it breaks down the sounds in such a way that it is able to recognize individual words. More sophisticated programs use machine learning to improve their accuracy. Such systems are able to learn accents, different pitch, and tones of voice, etc.
How to create a speech recognition system?
Any program that requires machine learning will require a team of expert developers. If you have such developers then they will be able to build a speech recognition program for you. If you don’t, however, then you should onboard developers from an experienced software development platform such as DevTeam.Space.