Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition

We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representation of the test posteriors using this dictionary enables projection to the space of training data. Relying on the fact that the intrinsic dimensions of the posterior subspaces are indeed very small and the matrix of all posteriors belonging to a class has a very low rank, we demonstrate how low-dimensional structures enable further enhancement of the posteriors and rectify the spurious errors due to mismatch conditions. The enhanced acoustic modeling method leads to improvements in continuous speech recognition task using hybrid DNN-HMM (hidden Markov model) framework in both clean and noisy conditions, where upto 15.4% relative reduction in word error rate (WER) is achieved

RECOGNITION AND ESTIMATION OF HUMAN LOCOMOTION WITH HIDDEN MARKOV MODELS

INTRODUCTION: The Collaborative Research Centre “Humanoid Robots” situated at the University of Karlsruhe is aimed to construct a learning and cooperating service robot. To cope with its tasks it is necessary that the robot is able to identify diverse objects as well as different persons. Looking at stochastic models for pattern recognition Hidden Markov Models (HMMs) are described to be most suitable to classify time arranged data (Bilmes 2002). The objective of this study is to screen if the HMMs supply satisfying rates of recognition of human trajectory and angle data. METHOD: Kinematic data of eight men and three women was captured at different walking and running speed (1.2 m/s, 3 m/s, 4 m/s, 5 m/s) on a treadmill. Data acquisition was realised with an infrared camera system with a frequency of 250Hz. For each walking/running speed there were 120 gait cycles of every test person available. The construction and training of the stochastic model was based on the gait data. Due to the fixed sequence of gait phases a HMM with a simple linear topology was chosen. Each state of the HMM represented a phase of the gait cycle. The different states were equipped with Gaussian distributions and transition probabilities to model the run of the angles observed. The HMM modelling human gait best was selected and trained with data of 17 double gait cycles for each data sequence of every test person. RESULTS: The trained HMMs showed recognition rates from 63% to 100% for the observed data sequences for five male test persons. Highest rates could be obtained with Centre of Mass and head angles. For some test person recognition rates decreased with data of gait cycles that were captured towards the end of one run. DISCUSSION: The high recognition rates based on kinematic data of Centre of Mass were expected due to the different mean values of the test persons according to their body height. The decrease of recognition rates that could be observed at some of the test person on late data of one run seems to be caused by acclimatisation to treadmill running. The achieved recognition rates exceed rates typical for speech recognition (Rabiner 1989). A combination of different angle data seems to promise increasing recognition rates. CONCLUSION: The study showed that HMMs seem to be suitable to identify humans based on their kinematic gait data satisfyingly stable. According to dislocation of the Gaussian distributions it could be possible to suggest on systematic changes on patterns over changes in walking-/running speed. REFERENCES: Bilmes, J. (2002). What HMMs Can Do. UWEE Technical Report, No UWEETR-2002-2003, University of Washington, Dept. of EE. Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77 (2), 257-286 Acknowledgement V. Wank, Institute of Sport Science, University of Tübingen German Research Foundation – CRC 588 Humanoid Robot

Forecasting negative effects of monotony and sensation seeking on performance during a vigilance task

The driving task requires sustained attention during prolonged periods, and can be performed in highly predictable or repetitive environments. Such conditions could create hypovigilance and impair performance towards critical events. Identifying such impairment in monotonous conditions has been a major subject of research, but no research to date has attempted to predict it in real-time. This pilot study aims to show that performance decrements due to monotonous tasks can be predicted through mathematical modelling taking into account sensation seeking levels. A short vigilance task sensitive to short periods of lapses of vigilance called Sustained Attention to Response Task is used to assess participants‟ performance. The framework for prediction developed on this task could be extended to a monotonous driving task. A Hidden Markov Model (HMM) is proposed to predict participants‟ lapses in alertness. Driver‟s vigilance evolution is modelled as a hidden state and is correlated to a surrogate measure: the participant‟s reactions time. This experiment shows that the monotony of the task can lead to an important decline in performance in less than five minutes. This impairment can be predicted four minutes in advance with an 86% accuracy using HMMs. This experiment showed that mathematical models such as HMM can efficiently predict hypovigilance through surrogate measures. The presented model could result in the development of an in-vehicle device that detects driver hypovigilance in advance and warn the driver accordingly, thus offering the potential to enhance road safety and prevent road crashes

Using hidden Markov models in credit card transaction fraud detection

In this paper we shall propose a credit card transaction fraud detection framework which uses Hidden Markov Models, a well established technology that has not as yet been tested in this area and through which we aim to address the limitations posed by currently used technologies. Hidden Markov Models have for many years been effectively implemented in other similar areas. The flexibility offered by these models together with the similarity in concepts between Automatic Speech Recognition and credit card fraud detection has instigated the idea of testing the usefulness of these models in our area of research. The study performed in this project investigated the utilisation of Hidden Markov Models by means of proposing a number of different frameworks, which frameworks are supported through the use of clustering and profiling mechanisms. The profiling mechanisms are used in order to build Hidden Markov Models which are more specialised and thus are deployed on training data that is specific to a set of cardholders which have similar spending behaviours. Clustering techniques were used in order to establish the association between different classes of transactions. Two different clustering algorithms were tested in order to determine the most effective one. Also, different Hidden Markov Models were built using different criteria for test data. The positive results achieved portray the effectiveness of these models in classifying fraudulent and legitimate transactions through a resultant percentage value which indicates the prominence of the transaction being contained in the respective model.peer-reviewe

On-line detection method for outliers of dynamic instability measurement data in geological exploration control process

Considering the characteristics of the vibration data detected by the unstable regulation process in the grinding and grading control system and the shortcomings of the traditional wavelet anomaly detection method, an online anomaly detection method combining autoregressive and wavelet analysis is proposed. By introducing the improved robust AR model, this method can overcome the problem that the time and frequency of traditional anomaly detection using wavelet analysis method cannot be well balanced and ensure the rationality of normal detection of process data. Considering the characteristics of parameter change and dynamic characteristics in the process of grinding and grading, the proposed method has the ability of on-line detection and parameter updating in real time, which ensures the control parameters of time-varying process control system. In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold, introduce the HMM to analyse the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the distribution of the abnormal value of the process data. Through the experiment and application, it is proven that the anomaly data detection method proposed in this paper is more suitable for the detection data in the process of unstable regulation

Modelling non-linear Spatial Market Integration and Equilibrium Processes in Hidden Markov Framework

Along the basic rationale of the Enke-Samuelson-Takajama-Judge spatial equilibrium theory and the dynamic conceptualizations made from arbitrage processes, the study explores regime-switching techniques in hidden Markov framework. This is motivated by complex non-linear structure inherent in market integration processes, which is derived from multiple equilibria conditions, and transaction costs constrained threshold autoregressive (TAR) effects. These place theoretical limitations on current time series empirical models that are applied in market integration studies. In equilibrium representation, the non-linearities imposed by both alternating rent levels and switching adjustment parameters are directly accommodated. Two synthesized time series market data sets of varying levels of non-linear structures are used to highlight the strengths and limitations of the Markov variants vis-à-vis the band-TAR models that have currently dominated market integration analysis. The former model could capture alternating adjustment processes implied by the relatively complex non-linear market data set while the later produced mixed results

In Silico Generation of Alternative Hypotheses Using Causal Mapping (CMAP)

Previously, we introduced causal mapping (CMAP) as an easy to use systems biology tool for studying the behavior of biological processes that occur at the cellular and molecular level. CMAP is a coarse-grained graphical modeling approach in which the system of interest is modeled as an interaction map between functional elements of the system, in a manner similar to portrayals of signaling pathways commonly used by molecular cell biologists. CMAP describes details of the interactions while maintaining the simplicity of other qualitative methods (e.g., Boolean networks)

Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks