Classification – Machine Learning

23 August 202013 June 2023

Recurrent Neural Networks for Sentiment Analysis

We described in one of the previous posts how to use convolutional neural networks, in order to perform speech recognition related to simple numbers from zero to nine.

In practice, speech recognition has superior performance by adopting particular neural networks called Recurrent Neural Networks, or simply RNNs.

Unlike “simple” feed-forward neural networks, RNNs process as input both the data currently provided as such, plus some of the output data provided retroactively. This allows them to work “with memory“.

25 May 202021 November 2021

Neural networks and speech recognition

Deep-learning ASR convolutional-neural-networks

In this post we are going to see an example of CNN (convolutional neural networks) applied to speech recognition application.
The goal of our machine learning model based on CNN’s Deep Learning algorithms will be to classify some simple words, starting with numbers from zero to nine.

To extract the distinctive features of speech, we will first adopt a voice coding procedure rather used in the ASR area (Automatic Speech Recognition) named Mel Frequency Cepstral Coefficient or more simply MFCC.

Thanks to the MFCC technique we will be able to encode every single word spoken vocally into a sequence of vectors, each of them 13 value-long representing the MFCC algorithm coefficients.

In our case – being the single words represented by single-digit numbers – we will go to encode each single number by using a 48 x 13 matrix.

The previous image shows the chain of the main modules involved during an MFCC encoding process: the voice signal is segmented into several frames of proper duration in the time domain (generally 25-40 ms).

16 May 202021 November 2021

Random forest

In this post we are going to manage a Classification problem, by using some CART models (Classification And Regression Trees).

We will use the following Bank Marketing Data Set dataset, provided by the UCI Machine Learning Repository:
ref. [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014

These are the results about some direct marketing campaigns carried out by a Portuguese bank by using outbound contact center calls, to try to sell repo financial products to customers.
The labeled output data we are interested in predicting are “binary” (column y): “yes” in the event that customers have accepted the bank deposit offer or “no” if the offer has been rejected.

Let’s import some useful libraries with scikit-learn: