In prior work we have demonstrated the noise robustness of a novel microphone solution, the PARAT earplug communication terminal. Here we extend that work with results for the ETSI Advanced Front-End and segmental cepstral mean and variance normalization (CMVN). We also propose a method for doing CMVN in the model domain. This removes the need to train models on normalized features, which may s...

Most compensation methods for robust speech recognition against noise assume independency between speech, additive and convolutive noise. However, the nonlinear nature distortion caused by noise may introduce correlation between noise and speech. To tackle this issue, we propose generalized-log spectral mean normalization (GLSMN) in which log spectral mean normalization (LSMN) is carried out in...

In this paper, we propose an effective feature normalization scheme based on eigenspace normalization, for achieving robust speech recognition. In general, Mean and Variance Normalization (MVN) is implemented in cepstral domain. However, another MVN approach using eigenspace was recently introduced, in that the eigenspace normalization procedure performs normalization in a single eigenspace. Th...

MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control, and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis, and response to treatment have been identified. Within this exciting field of rese...

The noise robustness property for an automatic speech recognition system is one of the most important factors to determine its recognition accuracy under a noise-corrupted environment. Among the various approaches, normalizing the statistical quantities of speech features is a very promising direction to create more noise-robust features. The related feature normalization approaches include cep...

A novel approach to speech rate normalization is presented. Models are constructed to model the way in which speech rate variation of a specific speaker influences the duration of phonemes. The models are evaluated in two ways. Firstly, the mean square error in phoneme duration based on our normalization is compared to the same error when such normalization is not applied. The second evaluation...

Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called batch normalization uses the distribution of the summed input to a neuron over a mini-batch of training cases to compute a mean and variance which are then used to normalize the summed input to tha...

Cepstral Mean Normalization (CMN) is a widely used technique for channel compensation and for noise robustness. CMN compensates for noise by transforming both train and test utterances to zero mean, thus matching first-order moment of train and test conditions. Since all utterances are normalized to zero mean, CMN could lead to loss of discriminative speech information, especially for short utt...