Investigating NMF Speech Enhancement for Neural Network based Acoustic Models

In the light of the improvements that were made in the last years with neural network-based acoustic models, it is an interesting question whether these models are also suited for noise-robust recognition. This has not yet been fully explored, although first experiments confirm this question. Furthermore, preprocessing techniques that improve the robustness should be re-evaluated with these new models. In this work, we present experimental results to address these questions. Acoustic models based on Gaussian mixture models (GMMs), deep neural networks (DNNs), and long short-term memory (LSTM) recurrent neural networks (which have an improved ability to exploit context) are evaluated for their robustness after clean or multi-condition training. In addition, the influence of non-negative matrix factorization (NMF) for speech enhancement is investigated. Experiments are performed with the Aurora-4 database and the results show that DNNs perform slightly better than LSTMs and, as expected, both beat GMMs. Furthermore, speech enhancement is capable of improving the DNN result. Index Terms: robust speech recognition, long short-term memory, speech enhancemen

Progress in Multi-Disciplinary Data Life Cycle Management

Modern science is most often driven by data. Improvements in state-of-the-art technologies and methods in many scientific disciplines lead not only to increasing data rates, but also to the need to improve or even completely overhaul their data life cycle management. Communities usually face two kinds of challenges: generic ones like federated authorization and authentication infrastructures and data preservation, and ones that are specific to their community and their respective data life cycle. In practice, the specific requirements often hinder the use of generic tools and methods. The German Helmholtz Association project "Large-Scale Data Management and Analysis" (LSDMA) addresses both challenges: its five Data Life Cycle Labs (DLCLs) closely collaborate with communities in joint research and development to optimize the communities data life cycle management, while its Data Services Integration Team (DSIT) provides generic data tools and services. We present most recent developments and results from the DLCLs covering communities ranging from heavy ion physics and photon science to high-throughput microscopy, and from DSIT

Joint Cooperation Between Humans and Cognitive Systems for Complex Task Solving

In this paper, we present an overview on recent advances in our work on human-machine communication between humans and technical cognitive systems in order to enable the solution of complex problems that require the joint cooperation of humans and intelligent systems in order to accomplish a challenging task that can be only successfully handled if both interact in a cooperative manner. An example for such cooperation would be the joint assembly of a heavy part by a human assisted by a robot during the manufacturing process of a car. It will be demonstrated in this paper that such challenges in joint cooperation occur very often in intelligent manufacturing environments which provide a very rich application scenario for the successful deployment of advanced pattern recognition algorithms

Optimization of data life cycles

Data play a central role in most fields of science. In recent years, the amount of data from experiment, observation, and simulation has increased rapidly and data complexity has grown. Also, communities and shared storage have become geographically more distributed. Therefore, methods and techniques applied to scientific data need to be revised and partially be replaced, while keeping the community-specific needs in focus.The German Helmholtz Association project "Large Scale Data Management and Analysis" (LSDMA) aims to maximize the efficiency of data life cycles in different research areas, ranging from high energy physics to systems biology. In its five Data Life Cycle Labs (DLCLs), data experts closely collaborate with the communities in joint research and development to optimize the respective data life cycle. In addition, the Data Services Integration Team (DSIT) provides data analysis tools and services which are common to several DLCLs. This paper describes the various activities within LSDMA and focuses on the work performed in the DLCLs

Memory-enhanced neural networks and NMF for robust ASR

In this article we address the problem of distant speech recognition for reverberant noisy environments. Speech enhancement methods, e. g., using non-negative matrix factorization (NMF), are succesful in improving the robustness of ASR systems. Furthermore, discriminative training and feature transformations are employed to increase the robustness of traditional systems using Gaussian mixture models (GMM). On the other hand, acoustic models based on deep neural networks (DNN) were recently shown to outperform GMMs. In this work, we combine a state-of-the art GMM system with a deep Long Short-Term Memory (LSTM) recurrent neural network in a double-stream architecture. Such networks use memory cells in the hidden units, enabling them to learn long-range temporal context, and thus increasing the robustness against noise and reverberation. The network is trained to predict frame-wise phoneme estimates, which are converted into observation likelihoods to be used as an acoustic model. It is of particular interest whether the LSTM system is capable of improving a robust stateof-the-art GMM system, which is confirmed in the experimental results. In addition, we investigate the efficiency of NMF for speech enhancement on the front-end side. Experiments are conducted on the medium-vocabulary task of the 2nd ‘CHiME’ Speech Separation and Recognition Challenge, which includes reverberation and highly variable noise. Experimental results show that the average word error rate of the challenge baseline is reduced by 64 % relative. The best challenge entry, a noiserobust state-of-the-art recognition system, is outperformed by 25 % relative

Advancing data management and analysis in different scientific disciplines

Over the past several years, rapid growth of data has affected many fields of science. This has often resulted in the need for overhauling or exchanging the tools and approaches in the disciplines' data life cycles. However, this allows the application of new data analysis methods and facilitates improved data sharing.The project Large-Scale Data Management and Analysis (LSDMA) of the German Helmholtz Association has been addressing both specific and generic requirements in its data life cycle successfully since 2012. Its data scientists work together with researchers from the fields such as climatology, energy and neuroscience to improve the community-specific data life cycles, in several cases even all stages of the data life cycle, i.e. from data acquisition to data archival. LSDMA scientists also study methods and tools that are of importance to many communities, e.g. data repositories and authentication and authorization infrastructure