Deep Temporal-Recurrent-Replicated-Softmax for Topical Trends over Time

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.Comment: In Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2018

Comparison of Four Approaches to Age and Gender Recognition for Telephone Applications

This paper presents a comparative study of four different ap-proaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with Human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dy-namic Bayesian networks to combine several prosodic features; (3) a system based solely on linear prediction analysis; and (4) Gaus-sian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodic features however shows very little dependence on the length of the utterance. Index Terms — speech processing, acoustic signal analysis, speaker classification, age, gender 1

Auswerteverfahren für die intelligente Identifikation organischer Stoffe

One way of identifying organic materia is to employ the Near Infrared Spectroscopy. This is done by analysing the absorption spectra of organic compounds in the Near Infrared by means of multivariate statistics. Existing methods for the analysis of organic materia need to be calibrated for every application. In this document an analysis method is described which adapts itself automatically to the organic materia to be identified. This automatic calibration system is called IntellIdent. It enables users even without experience to solve varying identification tasks concerning organic materia. It can therefore be employed in the field of sorting organic materia. IntellIdent uses the chemical similarity of organic compounds calculating global factors by means of the factor analysis. With these global factors the spectrum of almost every organic compound can be reconstructed. A selection process for the three optimal factors for an actual application is introduced. The position of the spectra in the factor space created by these three optimal factors is analysed employing the Mahalanobis Distance. IntellIdent is tested analysing different sorts of plastics, paper, wood and plants as well as contaminated soil

Auswerteverfahren für die intelligente Identifikation organischer Stoffe

One way of identifying organic materia is to employ the Near Infrared Spectroscopy. This is done by analysing the absorption spectra of organic compounds in the Near Infrared by means of multivariate statistics. Existing methods for the analysis of organic materia need to be calibrated for every application. In this document an analysis method is described which adapts itself automatically to the organic materia to be identified. This automatic calibration system is called IntellIdent. It enables users even without experience to solve varying identification tasks concerning organic materia. It can therefore be employed in the field of sorting organic materia. IntellIdent uses the chemical similarity of organic compounds calculating global factors by means of the factor analysis. With these global factors the spectrum of almost every organic compound can be reconstructed. A selection process for the three optimal factors for an actual application is introduced. The position of the spectra in the factor space created by these three optimal factors is analysed employing the Mahalanobis Distance. IntellIdent is tested analysing different sorts of plastics, paper, wood and plants as well as contaminated soil

Investigations On The Combination Of Four Algorithms To Increase The Noise Robustness Of A Dsr Front-End For Real World Car Data

This paper shows how the noise robustness of a MFCC feature extraction front-end can be improved by integrating four noise robustness algorithms being a Spectral Attenuation -, a Noise Level Normalistion -, a Cepstral Mean Normalization - and a Frame Dropping algorithm