In this paper, we investigate employment of discriminatively trained acoustic features modeled by Subspace Gaussian Mixture Models (SGMMs) for Rich Transcription meeting recognition. More specifically, first, we focus on exploiting various types of complex features estimated using neural network combined with conventional cepstral features and modeled by standard HMM/GMMs and SGMMs. Then, outpu...

This paper analyzes the linear and non-linear relationship between non-performing loans bank profitability measured by Net Interest Margin for a sample of 74 Middle Eastern North African banks over period 2005–2020. We used System Generalized Method Moments (SGMM) as approach Panel Smooth Transition Regression (PSTR) model approach. The empirical results SGMM indicated that ratio NPLs negativel...

This report describes implementation of the standard i-vector-PLDA framework for the Kaldi speech recognition toolkit. The current existing speaker recognition system implementation is based on the Subspace Gaussian Mixture Model (SGMM) technique although it shares many similarities with the standard implementation. In our implementation, we modified the code so that it mimics the standard algo...

This paper gives an overview of acoustic modeling and search techniques for low-power embedded ASR decoders. Our design decisions prioritize memory bandwidth, which is the main driver in system power consumption. We evaluate three acoustic modeling approaches–Gaussian mixture model (GMM), subspace GMM (SGMM) and deep neural network (DNN)–and identify tradeoffs between memory bandwidth and recog...

In this paper, we explore new high-level features for language identification. The recently introduced Subspace Gaussian Mixture Models (SGMM) provide an elegant and efficient way for GMM acoustic modelling, with mean supervectors represented in a low-dimensional representative subspace. SGMMs also provide an efficient way of speaker adaptation by means of lowdimensional vectors. In our framewo...