In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay network (TDNN) models assess Altman’s EM Z″-score zones firms for sample 100 companies operating hotel industry Republic Serbia. Hence, accuracies 9580 forecasting ANNs trained period 2016 2021 are an...

1 Abstract The shift operator, dened as q x(t) = x(t+1), is the basis for almost all discrete-time models. It has been shown however, that linear models based on the shift operator suer problems when used to model lightly-damped-low-frequency (LDLF) systems, with poles near (1; 0) on the unit circle in the complex plane. This problem occurs under fast sampling conditions. As the sampling rate i...

1 Abstract The shift operator, dened as q x(t) = x(t+1), is the basis for almost all discrete-time models. It has been shown however, that linear models based on the shift operator suer problems when used to model lightly-damped-low-frequency (LDLF) systems, with poles near (1; 0) on the unit circle in the complex plane. This problem occurs under fast sampling conditions. As the sampling rate i...

The capabilities of Locally Recurrent Neural Networks (LRNNs) in performing on-line Signal Processing (SP) tasks are well known [1,3,5,6,10,11,14,15]. In particular one of the most popular architecture is the Multi Layer Perceptron (MLP) with linear IIR temporal filter synapses (IIR-MLP) [3,5,10,11,14,15]. IIR-MLP is theoretically motivated as a non-linear generalization of linear adaptive IIR ...

One of the limitations of linear adaptive echo cancellers is nonlinearities which are generated mainly in the loudspeaker. The complete acoustic channel can be modelled as a nonlinear system convolved with a linear dispersive echo channel. Two new acoustic echo canceller models are developed to improve nonlinear performance. The first model consists of a time-delay feedforward neural network (T...

This paper investigates different approaches in order to improve the performance of a speech recognition system for given speaker by using no more than 5 min from this speaker, and without exchanging data other users/speakers. Inspired federated learning paradigm, we consider speakers that have access personalized database their own speech, learn an acoustic model collaborate with network model...

In this paper, we present an approach which significantly improves the performances of automatic speech recognition systems (ASRSs) dedicated to Arabic language. We propose to combine a version of Learning Vector Quantization (LVQ) and Time Delay Neural Networks (TDNNs) using an autoregressive version (AR) of the backpropagation algorithm. The underlying idea of this approach consists in the in...

Abstract Personalized voice triggering is a key technology in assistants and serves as the first step for users to activate assistant. involves keyword spotting (KWS) speaker verification (SV). Conventional approaches this task include developing KWS SV systems separately. This paper proposes single system called multi-task deep cross-attention network (MTCANet) that simultaneously performs SV,...

The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, gets degraded with environmental noises, channel variation, physical behavioral changes in speaker. types Speaker related feature play crucial role improving systems. Gammatone Frequency Cepstral Coefficient (GFCC) features has been widely used to develop robust conventional machine ...

Differences in acoustic characteristics between children’s and adults’ speech degrade performance of automatic recognition systems when trained using are used to recognize speech. This degradation is due the mismatch training testing. One main sources difference vocal tract resonances (formant frequencies) adult child speakers. The present study aims reduce formant frequencies by modifying form...