Speech is the new mode of communication. We already see a shift from written to spoken word in many areas, and this trend will only grow in the coming years. From Apple’s Siri and Google Assistant to Microsoft’s Cortana and Amazon’s Echo, there are more ways to interact with our mobile devices using voice technology.
Today, speech recognition is becoming increasingly popular for businesses in their digital transformation journey. Since 2018, speech recognition in the US alone has increased by a whopping 31%, as more than 135 million people use voice search every month.
But as with any good technology, it still has some challenges. Businesses face different challenges when using speech recognition, so this blog will look at the challenges and how to overcome them.
One of the biggest challenges of speech recognition is accuracy. According to a recent Statista study, Rev.ai holds the top position regarding the accuracy of speech recognition technology, with 84% accuracy. While the accuracy of speech recognition has significantly improved, there’s a lot of room for improvement.
The problem is that speech is a very complex signal, and it can be affected by many different things, including background noise, accent, and pronunciation.
To improve your speech recognition system’s accuracy, you need to ensure that the training data used during the development process is representative of your application’s user base.
For example, if you are working on an English-speaking application, you should use only English-speaking users in your training dataset. Similarly, if you are working on a French-speaking application, you should use only French speakers as training data.
Unlike keyboards or touchscreens, speech recognition depends on your voice and the words you say to complete an action or process information. This means that the spoken words are stored in your device and could potentially be accessed by someone with malicious intent if not adequately secured against unauthorized access.
The first step towards securing users’ voice data is ensuring that all users have unique user tags within your application. These tags can be used to identify individual users when they log into your application for voice authentication purposes.
The second step is providing multiple layers of security through authentication protocols such as OAuth2.0, SAML2, RADIUS, and more. It’ll ensure only authorized users can access their profile information from any third-party applications they use. It also ensures that no one else can access these applications’ personal data.
Getting the speech recognition library to run in different environments may prove challenging. Also, speech recognition requires data solutions and other resources for deployment.
Deployment of a speech recognition system is challenging because of the following:
- There are many different languages and dialects, each with their own pronunciation, grammar, and vocabulary.
- The user may have an accent or a speech impediment that can make it difficult for the system to understand them.
- The user may speak too quickly or slowly for the system to recognize their voice.
- There are often background noises that can interfere with the ability of the system to understand what the user is saying.
To overcome the deployment issues, you must compile the library with different dependencies depending on what you’re trying to do (desktop, mobile, or embedded system deployment) and then deploy it in the appropriate environment with seamless integration.
Integrating high-quality audio and speech data for training your conversational AI model is important. You can access the dataset through APIs that you can incorporate into your application. This will allow you to deploy a more natural and lifelike conversation AI that understands any language well.
Language coverage refers to how many languages are supported by a particular speech recognition system. A good example of this is Google Assistant, which currently supports twelve languages, while Alexa supports eight.
Language coverage is a major challenge in speech recognition. As human language differs with different accents, the software must be able to understand all of them.
Even within one language, many dialects and accents can make it difficult for the software to recognize words correctly. The more languages you want your application to support, the more complicated it will become.
To overcome the language coverage challenge, developers need to use artificial intelligence, machine learning algorithms, and statistical models to improve their accuracy in understanding different languages.
Another thing you can do is get multilingual speech data from more than one source, then train your system using more information than just what you have in-house. This will help improve the accuracy of your system.
Overall, an effective speech recognition system should be easy to set up and use in various situations while achieving accurate results with little frustration on the user’s part.
The solutions mentioned above are just a few ways your speech recognition system can be improved and ensure that the technology needed is available. As innovations continually arise in speech recognition, it’ll be important to be vigilant and ensure that these systems continue to improve.