This paper focuses on the automatic determination of the age of children in preschool and primary school age. For each child a Gaussian Mixture Model (GMM) is trained. As training method the Maximum A Posteriori adaptation (MAP) is used. MAP derives the speaker models from a Universal Background Model (UBM) and does not perform an independent parameter estimation. The means of each GMM are extr...

This study proposes an acoustic model adaptation scheme to improve speech recognition in severely adverse environments utilizing untranscribed data. In the proposed method, a clean GMM is estimated from clean training data, and a noisecorrupted GMM is obtained by MAP adaptation over the adaptation data. The Gaussian component of the adapted HMMs is obtained using the transform of the most simil...

We have previously developed a one-to-many eigenvoice conversion (EVC) system enabling the conversion from a specific source speaker’s voice into an arbitrary target speaker’s voice. In this system, eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets composed of utterance pairs of the source and many pre-stored target speakers. The EV-GMM is effecti...

This paper presents in-car speech recognition using a modelbased Wiener filter (MBW) and multi-condition (MC) training. The MBW is a 2-step denoising algorithm based on both rough and precise estimation of speech signals. Correcting roughly estimated signals with a Gaussian mixture model (GMM) makes it possible to accurately denoise with little computational cost. In an evaluation of in-car spe...

Longitudinal data with time-dependent covariates is not readily analyzed as there are inherent, complex correlations due to the repeated measurements on the sampling unit and the feedback process between the covariates in one time period and the response in another. A generalized method of moments (GMM) logistic regression model (Lalonde, Wilson, and Yin 2014) is one method to analyze such corr...

This paper proposes a sequence-to-frame dynamic time warping (DTW) combination approach to improve out-ofvocabulary (OOV) spoken term detection (STD) performance gain. The goal of this paper is twofold: first, we propose a method that directly adopts the posterior probability of deep neural network (DNN) and Gaussian mixture model (GMM) as the similarity distance for sequence-to-frame DTW. Seco...

A two-stage classifier is used to improve the classification performance between normal and pathological voices. A primary classification between normal and pathological voices is achieved by the Gaussian mixture model (GMM) log-likelihood scores. For samples that do not meet the thresholds for normal or disordered voice in the GMM, the final decision is made by a higher-order statistics (HOS)-...

Speaker verification (SV) systems that employ maximum a posteriori (MAP) adaptation of a Gaussian mixture model (GMM) universal background model (UBM) incur a significant teststage computational load in the calculation of a posteriori probabilities and sufficient statistics. We propose a multi-layered hash system employing a tree-structured GMM which uses Runnalls’ GMM reduction technique. The ...