# Quartered Chirp Spectral Envelope for Whispered vs Normal Speech Classification

arXiv: https://arxiv.org/abs/2408.14777 · HTML: https://mlsanity.com/paper/2408.14777/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 4 of 4)

- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [LSTM-based Whisper Detection](https://mlsanity.com/paper/1809.07832/) (2018)
- [Significance of Chirp MFCC as a Feature in Speech and Audio Applications](https://mlsanity.com/paper/2402.12239/) (2024)
- [Quartered Spectral Envelope and 1D-CNN-based Classification of Normally Phonated and Whispered Speech](https://mlsanity.com/paper/2408.13746/) (2024)

## Abstract (arXiv)

Whispered speech as an acceptable form of human-computer interaction is gaining traction. Systems that address multiple modes of speech require a robust front-end speech classifier. Performance of whispered vs normal speech classification drops in the presence of additive white Gaussian noise, since normal speech takes on some of the characteristics of whispered speech. In this work, we propose a new feature named the quartered chirp spectral envelope, a combination of the chirp spectrum and the quartered spectral envelope, to classify whispered and normal speech. The chirp spectrum can be fine-tuned to obtain customized features for a given task, and the quartered spectral envelope has been proven to work especially well for the current task. The feature is trained on a one dimensional convolutional neural network, that captures the trends in the spectral envelope. The proposed system performs better than the state of the art, in the presence of white noise.