The role of digital transformation in creating value for commercial banks has been interesting to researchers a long time. While many have significantly investigated transformation, and managers still met difficulties examining the distribution business performance. This paper aims evaluate impact on Vietnamese banks’ performance by different sizes, from there proposing policy implications impr...

In this paper, we adress the problem of additive noise which degrades substantially the performances of speech recognition system. We propose a cepstral denoising based on the Subspace Gaussian Mixture Models paradigm (SGMM). The acoustic space is modeled by using a UBM-GMM. Each phoneme is modeled by a GMM derived from the UBM. The concatenation of the means of a given GMM leads to a very high...

We present an integrated system with structural Gaussian mixture models (SGMMs) and a neural network for purposes of achieving both computational efficiency and high accuracy in text-independent speaker verification. A structural background model (SBM) is constructed first by hierarchically clustering all Gaussian mixture components in a universal background model (UBM). In this way the acousti...

In conventional hidden Markov model (HMM) based speech recognisers, the emitting HMM states are modelled by Gaussian Mixture Models (GMMs), with parameters been estimated directly from the training data. However, in Subspace Gaussian mixture model(GMM) based acoustic modelling, the parameters of each state model are derived from the globally shared model subspaces which are normally low dimensi...

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...

We present a radio browsing system developed on a very small corpus of annotated speech by using semi-supervised training of multilingual DNN/HMM acoustic models. This system is intended to support relief and developmental programmes by the United Nations (UN) in parts of Africa where the spoken languages are extremely under resourced. We assume the availability of 12 minutes of annotated speec...