In this chapter, we demonstrated many techniques for processing speech and images. This capability is becoming important, as electronic devices are increasingly embracing these communication mediums.

TTS was demonstrated using FreeTSS. This technique allows a computer to present results as speech as opposed to text. We learned how we can control the attributes of the voice used, such as its gender and age.

Recognizing speech is useful and helps bridge the human-computer interface gap. We demonstrated how CMUSphinx is used to recognize human speech. As there is often more than one way speech can be interpreted, we learned how the API can return various options. We also demonstrated how individual words are extracted, along with the relative confidence that the right word was identified.

Image processing is a critical aspect of many applications. We started our discussion of image processing by use Tess4J to extract text from an image. This process is sometimes referred to as OCR. We learned, as with many visual and audio data files, that the quality of the results is related to the quality of the image.

We also learned how to use OpenCV to identify faces within an image. Information about specific view of faces, such as frontal or profile views, are contained in XML files. These files were used to outline faces within an image. More than one face can be detected at a time.

It can be helpful to classify images and sometimes external tools are useful for this purpose. We examined Neuroph Studio and created a neural network designed to recognize and classify images. We then tested our network with images of human faces.

In the next chapter, we will learn how to speed up common data science applications using multiple processors.