Estimation of Dynamic Latent Variable Models Using Simulated Nonparametric Moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models.dynamic latent variable models; simulation-based estimation; simulated moments; kernel regression; nonparametric estimation

Estimation of dynamic latent variable models using simulated nonparametric moments

Abstract. Given a model that can be simulated, conditional moments at a trial parameter value can be calculated with high accuracy by applying kernel smoothing methods to a long simulation. With such conditional moments in hand, standard method of moments techniques can be used to estimate the parameter. Because conditional moments are calculated using kernel smoothing rather than simple averaging, it is not necessary that the model be simulable subject to the conditioning information that is used to define the moment conditions. For this reason, the proposed estimator is applicable to general dynamic latent variable models. It is shown that as the number of simulations diverges, the estimator is consistent and a higher-order expansion reveals the stochastic difference between the infeasible GMM estimator based on the same moment conditions and the simulated version. In particular, we show how to adjust standard errors to account for the simulations. Monte Carlo results show how the estimator may be applied to a range of dynamic latent variable (DLV) models, and that it performs well in comparison to several other estimators that have been proposed for DLV models

Speaker Recognition Using Machine Learning Techniques

Speaker recognition is a technique of identifying the person talking to a machine using the voice features and acoustics. It has multiple applications ranging in the fields of Human Computer Interaction (HCI), biometrics, security, and Internet of Things (IoT). With the advancements in technology, hardware is getting powerful and software is becoming smarter. Subsequently, the utilization of devices to interact effectively with humans and performing complex calculations is also increasing. This is where speaker recognition is important as it facilitates a seamless communication between humans and computers. Additionally, the field of security has seen a rise in biometrics. At present, multiple biometric techniques co-exist with each other, for instance, iris, fingerprint, voice, facial, and more. Voice is one metric which apart from being natural to the users, provides comparable and sometimes even higher levels of security when compared to some traditional biometric approaches. Hence, it is a widely accepted form of biometric technique and is constantly being studied by scientists for further improvements. This study aims to evaluate different pre-processing, feature extraction, and machine learning techniques on audios recorded in unconstrained and natural environments to determine which combination of these works well for speaker recognition and classification. Thus, the report presents several methods of audio pre- processing like trimming, split and merge, noise reduction, and vocal enhancements to enhance the audios obtained from real-world situations. Additionally, a text-independent approach is used in this research which makes the model flexible to multiple languages. Mel Frequency Cepstral Coefficients (MFCC) are extracted for each audio, along with their differentials and accelerations to evaluate machine learning classification techniques such as kNN, Support Vector Machines, and Random Forest Classifiers. Lastly, the approaches are evaluated against existing research to study which techniques performs well on these sets of audio recordings