Javier Ramírez

TL;DR: A new VAD algorithm for improving speech detection robustness in noisy environments and the performance of speech recognition systems is presented, which formsulates the speech/non-speech decision rule by comparing the long-term spectral envelope to the average noise spectrum, thus yielding a high discriminating decision rule and minimizing the average number of decision errors.

TL;DR: In this paper, deep belief networks are applied on brain regions defined by the Automated Anatomical Labeling (AAL) atlas and the final prediction is determined by a voting scheme, where discriminative features are computed in an unsupervised fashion.

TL;DR: This chapter shows a comprehensive approximation to the main challenges in voice activity detection, the different solutions that have been reported in a complete review of the state of the art and the evaluation frameworks that are normally used.

TL;DR: A new CAD system that allows the early AD diagnosis using tissue-segmented brain images and is based on several multivariate approaches, such as partial least squares (PLS) and principal component analysis (PCA), which aims to discriminate between AD, mild cognitive impairment (MCI) and elderly normal control (NC) subjects.

TL;DR: This letter presents a new voice activity detector (VAD) for improving speech detection robustness in noisy environments and the performance of speech recognition systems using an optimum likelihood ratio test (LRT) involving multiple and independent observations.