# A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN

arXiv: https://arxiv.org/abs/2409.14348 · HTML: https://mlsanity.com/paper/2409.14348/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 59 references that are in the map.
- First posted on arXiv 22 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods](https://mlsanity.com/paper/1908.10623/) (2019)
- [Dialect Identification of Spoken North S'ami Language Varieties Using Prosodic Features](https://mlsanity.com/paper/2003.10183/) (2020)
- [Convolutional Speech Recognition with Pitch and Voice Quality Features](https://mlsanity.com/paper/2009.01309/) (2020)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [Literary and Colloquial Tamil Dialect Identification](https://mlsanity.com/paper/2408.13739/) (2024)
- [Literary and Colloquial Dialect Identification for Tamil using Acoustic Features](https://mlsanity.com/paper/2408.14887/) (2024)

## Abstract (arXiv)

In ideal human computer interaction (HCI), the colloquial form of a language would be preferred by most users, since it is the form used in their day-to-day conversations. However, there is also an undeniable necessity to preserve the formal literary form. By embracing the new and preserving the old, both service to the common man (practicality) and service to the language itself (conservation) can be rendered. Hence, it is ideal for computers to have the ability to accept, process, and converse in both forms of the language, as required. To address this, it is first necessary to identify the form of the input speech, which in the current work is between literary and colloquial Tamil speech. Such a front-end system must consist of a simple, effective, and lightweight classifier that is trained on a few effective features that are capable of capturing the underlying patterns of the speech signal. To accomplish this, a one-dimensional convolutional neural network (1D-CNN) that learns the envelope of features across time, is proposed. The network is trained on a select number of handcrafted features initially, and then on Mel frequency cepstral coefficients (MFCC) for comparison. The handcrafted features were selected to address various aspects of speech such as the spectral and temporal characteristics, prosody, and voice quality. The features are initially analyzed by considering ten parallel utterances and observing the trend of each feature with respect to time. The proposed 1D-CNN, trained using the handcrafted features, offers an F1 score of 0.9803, while that trained on the MFCC offers an F1 score of 0.9895. In light of this, feature ablation and feature combination are explored. When the best ranked handcrafted features, from the feature ablation study, are combined with the MFCC, they offer the best results with an F1 score of 0.9946.