Further learning

• Other languages: The ASR dataset from which we used the Scottish language dataset also contains many other languages, such as Sinhala, and many Indian languages, such as Hindi, Marathi, and Bengali. The next logical step would be to try this ASR model for another language and compare the results. It is also a great way to learn how to manage training requirements as some of the audio files in these datasets are bigger; hence, they will need more compute power.

Many non-English languages don't have apps widely available on mobiles (for example, the Marathi language spoken in India) and a lack of technical tools in native languages limits the adoption of many tools in remote parts of the world. Creating an ASR in your local language can add great value to the technical ecosystem as well.

• Audio and video together: Another interesting task is to combine the audio speech recognition and video classification tasks that we have seen today and use...