You are a machine learning specialist working for a translation service company.
Your company offers several mobile applications used for translation on smartphones and tablets.
As a new feature of one of your translation apps, your company offers a feature to generate handwritten notes from spoken text. Which algorithm is the best choice for your new feature?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is correct.
The Long Short-Term Memory (LSTM) can work with sequences of spoken language and can be used to generate sequenced output such as handwritten text.
Option B is incorrect.
Convolutional Neural Networks are primarily used to work with image data.
You are working with sound data, spoken text.
Option C is incorrect.
The Multilayer Perceptron algorithm is used primarily for classification predictions and regression predictions.
Your problem to solve is to convert spoken text to handwritten text.
Option D is incorrect.
The Support Vector Machine algorithm is primarily used for classification, regression, and anomaly detection.
Your problem to solve is to convert spoken text to handwritten text.
Reference:
Please see the Machine Learning Mastery article titled When to Use MLP, CNN, and RNN Neural Networks (https://machinelearningmastery.com/when-to-use-mlp-cnn-and-rnn-neural-networks/), the SciKit Learn page titled 1.4
Support Vector Machines (https://scikit-learn.org/stable/modules/svm.html), and the Wikipedia page titled Long short-term memory (https://en.wikipedia.org/wiki/Long_short-term_memory#:~:text=Long%20short%2Dterm%20memory%20(LSTM)%20is%20an%20artificial%20recurrent,the%20field%20of%20deep%20learning.&text=LSTM%20networks%20are%20well%2Dsuited,events%20in%20a%20time%20series.)
For generating handwritten notes from spoken text, the best algorithm choice would be Long Short-Term Memory (LSTM) neural network.
LSTM is a type of recurrent neural network (RNN) that can handle long sequences of input data, such as spoken text. LSTM is suitable for natural language processing (NLP) tasks, which require modeling the long-term dependencies between words and phrases.
The handwritten notes generation requires the model to learn the relationship between the spoken text and the corresponding handwriting. LSTM is well-suited for this task as it can capture the complex dependencies between the input spoken text and the output handwritten notes.
Convolutional Neural Networks (CNN) are typically used for image recognition tasks, and may not be the best choice for this task. Multilayer Perceptron (MLP) is a type of feedforward neural network and is not suitable for handling sequence data. Support Vector Machines (SVM) are a type of supervised learning algorithm used for classification and regression analysis, but not appropriate for this task.
Therefore, LSTM is the best algorithm choice for generating handwritten notes from spoken text.