S. R. M. Prasanna

Parismita Gogoi, Sishir Kalita, Wendy Lalhminghlui et al.

This study explores the use of self-supervised learning (SSL) models for tone recognition in three low-resource languages from North Eastern India: Angami, Ao, and Mizo. We evaluate four Wav2vec2.0 base models that were pre-trained on both tonal and non-tonal languages. We analyze tone-wise performance across the layers for all three languages and compare the different models. Our results show that tone recognition works best for Mizo and worst for Angami. The middle layers of the SSL models are the most important for tone recognition, regardless of the pre-training language, i.e. tonal or non-tonal. We have also found that the tone inventory, tone types, and dialectal variations affect tone recognition. These findings provide useful insights into the strengths and weaknesses of SSL-based embeddings for tonal languages and highlight the potential for improving tone recognition in low-resource settings. The source code is available at GitHub 1 .

Mrinmoy Bhattacharjee, S. R. M. Prasanna, Prithwijit Guha

Distinct striation patterns are observed in the spectrograms of speech and music. This motivated us to propose three novel time-frequency features for speech-music classification. These features are extracted in two stages. First, a preset number of prominent spectral peak locations are identified from the spectra of each frame. These important peak locations obtained from each frame are used to form Spectral peak sequences (SPS) for an audio interval. In second stage, these SPS are treated as time series data of frequency locations. The proposed features are extracted as periodicity, average frequency and statistical attributes of these spectral peak sequences. Speech-music categorization is performed by learning binary classifiers on these features. We have experimented with Gaussian mixture models, support vector machine and random forest classifiers. Our proposal is validated on four datasets and benchmarked against three baseline approaches. Experimental results establish the validity of our proposal.