UPDATE July 25, 2018: I have changed the network into one chain of functions instead of two that had to be managed with another function. This version should also have the kernels oriented the right way to replicate the paper. Using convolutional layers for speech data There are a number …

Extracting the speech features in Julia God, finally! The code! Up until now, I have been trying to situate automatic speech recognition in the context of what we know about human speech because I believe this is important to be able to reason about the kind of data we're working …

In the last post, we discussed the acoustic basis of speech. In this post, we'll build on those concepts to discuss automatch speech recogntion in preparation for extracting the features we'll use as data in the next post. Automatic speech recognition Automatic speech recognition is the process of having computer …

In the last post, we discussed the articulatory aspects of speech. This post will build on that one and discuss the acoustic aspects of speech. The acoustic aspects of speech When speaking, a sound wave is produced. This is the air stream that is produced and modulated during the process …

Speech The act of producing speech is one of those activities we engage in virtually every day in hearing communities. It feels natural, and on the surface, it seems quite simple. On further inspection, however, it is remarkably complex and involves the coordination of a great number of body parts …

Deep learning for speech recognition blog series: Introduction This is the first in a series of posts documenting my process in setting up a deep learning speech recognition system described by Zhang et al. (2017) in Julia using the Flux machine learning package for my Google Summer of Code 2018 …