This paper compares the performance of two phonetic notations, IPA and ASJPcode, with the alphabetical notation for word-level language identification. Two machine learning models, a Multilayer Percerptron and a Logistic Regression model, are used to classify words using each of the three notations. With both models the IPA notation outperforms the other two notations.

Rhyming words are one of the most important features in poems. They add rhythm to a poem, and poets use this literary device to portray emotion and meaning to their readers. Thus, detecting rhyming words will aid in adding emotions and enhancing readability when generating poems. Previous studies have been done on the topic of poem generation. However, those works did not put too much emphasis on the rhyme detector. Thus, this research will solely focus on rhyme detection and its evaluation. The aim of this research is to determine the most accurate way of detecting whether two English words rhyme. English rhyming words will be detected using combinations of features. Five features are used: edit distance, hamming distance, jaccard similarity, longest common substring, and vowel and consonant weights. We also experiment with two methods of retrieving phonemes: using the entire phoneme translation, and using part of the phoneme translation. We find that using only hamming distance and jaccard similarity with part of the phoneme translation, we can already obtain an accuracy of 90.05% with a log loss of 0.25 when trained on a balanced dataset. The reason for this remains unclear because there is no clear separation between the two classes.