For my final project I used the Google Teachable Machine Audio Classifier to build a speech hangman game. The goal of the project was to use the audio classifier to clearly detect each letter of the alphabet. The hangman program is one output use for this trained model however there are many possible uses for an audio model trained on the alphabet.
First, I built a simple webpage with the hangman game (Figure 1). In this iteration I connected keyboard presses to the game. Thus the game is being played when the user presses keys on the computer keyboard.
I then trained a small model on just the letters “h” and “a” and was able to implement this model into the hangman example (Figure 2). When a wrong letter is guessed, more of the man is drawn and the letter in the alphabet turns red to indicate that you already guessed that letter.
I then trained a full model on the entire alphabet. Figure 3 shows the final example running with a fully trained alphabet model.
While this project works relatively well for this small hangman game, it is not a perfectly trained model. This project was a good example of how complex speech recognition algorithms need to be in order to be accurate. I notices that letters like “x” and “s” were harder to train since they sound quite similar. I actually ended up training the alphabet multiple times to try to get the best result for the hangman game. Hopefully, others can use this model for other implementations in the future.
Link to project: