Quartered Chirp Spectral Envelope for Whispered vs Normal Speech Classification
This work addresses a domain-specific problem for human-computer interaction systems that need robust speech classification, but it is incremental as it builds on existing spectral envelope techniques.
The paper tackled the problem of classifying whispered vs normal speech in noisy conditions by proposing a new feature called the quartered chirp spectral envelope, achieving better performance than state-of-the-art methods in the presence of white noise.
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.