Jesin James

Jesin James, Isabella Shields, Vithya Yogarajan et al.

Te reo Māori (referred to as Māori), New Zealand's indigenous language, is under-resourced in language technology. Māori speakers are bilingual, where Māori is code-switched with English. Unfortunately, there are minimal resources available for Māori language technology, language detection and code-switch detection between Māori-English pair. Both English and Māori use Roman-derived orthography making rule-based systems for detecting language and code-switching restrictive. Most Māori language detection is done manually by language experts. This research builds a Māori-English bilingual database of 66,016,807 words with word-level language annotation. The New Zealand Parliament Hansard debates reports were used to build the database. The language labels are assigned using language-specific rules and expert manual annotations. Words with the same spelling, but different meanings, exist for Māori and English. These words could not be categorised as Māori or English based on word-level language rules. Hence, manual annotations were necessary. An analysis reporting the various aspects of the database such as metadata, year-wise analysis, frequently occurring words, sentence length and N-grams is also reported. The database developed here is a valuable tool for future language and speech technology development for Aotearoa New Zealand. The methodology followed to label the database can also be followed by other low-resourced language pairs.

Jesin James, Balamurali B. T., Binu Abeysinghe et al.

This study investigates discriminative patterns learned by neural networks for accurate speech classification, with a specific focus on vowel classification tasks. By examining the activations and features of neural networks for vowel classification, we gain insights into what the networks "see" in spectrograms. Through the use of class activation mapping, we identify the frequencies that contribute to vowel classification and compare these findings with linguistic knowledge. Experiments on a American English dataset of vowels showcases the explainability of neural networks and provides valuable insights into the causes of misclassifications and their characteristics when differentiating them from unvoiced speech. This study not only enhances our understanding of the underlying acoustic cues in vowel classification but also offers opportunities for improving speech recognition by bridging the gap between abstract representations in neural networks and established linguistic knowledge