Mel-Scaled Autoregressive (Mel-AR) Model based Voice Activity Detection using Likelihood Ratio Measure

On The Mel-scaled Cepstrum

The mel-scaled cepstrum is a signal representation scheme used in the analysis of speech signals. Due to its reported superior performance, especially under adverse conditions, it is becoming an increasingly popular choice as feature extraction front end to spoken language systems. Having evolved over a period of more than fifty years, the mel-scaled cepstrum owes part of its heritage to the pa...

Recognition Of Voice Using Mel Cepstral Coefficient & Vector Quantization

Human Voice is characteristic for an individual. The ability to recognize the speaker by his/her voice can be a valuable biometric tool with enormous commercial as well as academic potential. Commercially, it can be utilized for ensuring secure access to any system. Academically, it can shed light on the speech processing abilities of the brain as well as speech mechanism. In fact, this feature...

Mel-Wiener Filter for Mel-LPC Based Speech Recognition

Mel-scaled Wavelet Filter Base Unvoiced Phoneme Re

In this paper we propose a filter bank structure derived by using admissible wavelet packet transform. These filters have Mel scale spacing and have an advantage of easy implementation with higher resolution in time-frequency domain because of wavelet transform. The features are obtained by first calculating the energy in each filter band and then applying the Discrete Cosine Transform (DCT) to...

Likelihood ratio test with complex laplacian model for voice activity detection

This paper proposes a voice activity detector (VAD) based on the complex Laplacian model. With the use of a goodness-of-fit (GOF) test, it is discovered that the Laplacian model is more suitable to describe noisy speech distribution than the conventional Gaussian model. The likelihood ratio (LR) based on the Laplacian model is computed and then applied to the VAD operation. According to the exp...