To overcome this problem, all we need to do is pad the output vectors with a constant value of 0. We use the most obvious and simple one, MFCC encoding, which is super effective for working with speech signals. There are a lot of techniques and python packages for audio feature extraction. An embedding is a mapping from discrete objects, such as words vectors of real numbers. We could use embedding to overcome this problem.

We need to prepare a fixed size vector for each audio file for classification.

We can't just feed an audio file to a CNN.

We have two critical problems for preparing data to feed into the neural network. (trimming)įinally, we sorted data by numbers, and the original audio and the noise files are stored in a folder with the word name as the label. To clear the data, we read the zero values after reading the audio signals from both sides. Thus, for each sound, we will have a signal with a specific sampling frequency the sampling frequency of the input dataset signals is 54,000 Hz, which we did not change. We will use one audio channel (mono) to read each voice. In this way, we will have about 500 data for each word to train our word recognition network. The extra noise power is calculated according to the power of each signal so that the audio signal is not completely damaged. Major frameworks/libraries used to this project:Ĭonsidering the lack of data in the collected database, we will add some noise to each data and use it as new data for network training. To show this capability, we have implemented the algorithm on the Raspberry Pi board. You should change the number of model category output and input dataset labels a bit.įor instance, the task in this project will be to classify audio between six words in the Farsi language (words meaning in the bracket):Īlthough single word speech recognition is not suitable for continuous speech recognition, it could be used to build a voice assistant or bot. There is no problem adding as much as you may wish. The end goal is to accurately identify a set of predefined words from short audio clips. In this project, everything starts from a spoken speech from which features are extracted to recognize which word was said (which turns into a classification task).

Report Bug & Request Feature Table of Contents Simple single persian word speech recognition using CNN on Raspberry Pi board 🗣ĭemo persian word speech recognition (Google Colab)

How to use cepstral voices raspberry pi