Speech Emotion Recognition System using Librosa for Better Customer Experience

Call center employees usually depend on instinct to judge a potential customer and how to pitch to them. In this paper, we pitch a more effective way for call center employees to generate more leads and engagement to generate higher revenue by analyzing the speech of the target customer by using machine learning practices and depending on data to make data-driven decisions rather than intuition. Speech Emotion Recognition otherwise known as SER is the demonstration of aspiring to perceive human inclination along with the behavior. Normally voice reflects basic feeling through tone and pitch. According to human behavior, many creatures other than human beings are also synced themselves. In this paper, we have used a python-based library named Librosa for examining music tones and sounds or speeches. In this regard, various libraries are being assembled to build a detection model utilizing an MLP (Multilayer Perceptron) classifier. The classifier will train to perceive feeling from multiple sound records. The whole implementation will be based on an existing Kaggle dataset for speech recognition. The training set will be treated to train the perceptron whereas the test set will showcase the accuracy of the model

Predicting self‐declared movie watching behavior using Facebook data and information‐fusion sensitivity analysis

The main purpose of this paper is to evaluate the feasibility of predicting whether yes or no a Facebook user has self-reported to have watched a given movie genre. Therefore, we apply a data analytical framework that (1) builds and evaluates several predictive models explaining self-declared movie watching behavior, and (2) provides insight into the importance of the predictors and their relationship with self-reported movie watching behavior. For the first outcome, we benchmark several algorithms (logistic regression, random forest, adaptive boosting, rotation forest, and naive Bayes) and evaluate their performance using the area under the receiver operating characteristic curve. For the second outcome, we evaluate variable importance and build partial dependence plots using information-fusion sensitivity analysis for different movie genres. To gather the data, we developed a custom native Facebook app. We resampled our dataset to make it representative of the general Facebook population with respect to age and gender. The results indicate that adaptive boosting outperforms all other algorithms. Time- and frequency-based variables related to media (movies, videos, and music) consumption constitute the list of top variables. To the best of our knowledge, this study is the first to fit predictive models of self-reported movie watching behavior and provide insights into the relationships that govern these models. Our models can be used as a decision tool for movie producers to target potential movie-watchers and market their movies more efficiently

Using support vector machine ensembles for target audience classification on Twitter

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results show that the methods presented are able to successfully identify a target audience with high accuracy. In addition, we show that using a statistical inference approach such as bootstrapping in over-sampling, instead of using random sampling, to construct training datasets can achieve a better classifier in an SVM ensemble. We conclude that such an ensemble system can take advantage of data diversity, which enables real-world applications for differentiating prospective customers from the general audience, leading to business advantage in the crowded social media space