vSpeech technologies have become integral to everyday life and have many applications. For instance, with Automatic Speech Recognition (ASR), you can control devices and access information simply by using voice commands. Additionally, a speech-to-text application allows for real-time dictation and not taking, while a speaker identification application can be used to identify who is speaking in an audio sample. This technology can also aid communication with people who speak different languages by converting spoken audio from one language to another through language translation applications. These are just a few examples of the many tasks that speech recognition technology can be used for.

However, as this technology increases in popularity, we are uncovering its fragility. For example, imagine you're trying to use a virtual assistant like Siri, Alexa or OK Google. But no matter how hard you try, the assistant seems to have trouble understanding you. Even though you're a bilingual or native speaker, you have to repeat yourself multiple times or even change your accent for the assistant to understand you finally. This unfair behaviour, also called bias, refers to the tendency to perform differently for certain factors such as age, gender, and accent, among others. To mitigate bias, it is essential to use diverse training data and continually evaluate and enhance the system's performance on underrepresented groups.