The effect of a nonresonant radiative field on low-energy rotationally inelastic Na++N2\text{Na}^{+} + \text{N}_2 collisions

We examine the effects of a linearly polarized nonresonant radiative field on the dynamics of rotationally inelastic $\text{Na}^{+} + \text{N}_2$ collisions at eV collision energies. Our treatment is based on the Fraunhofer model of matter wave scattering and its recent extension to collisions in electric fields [arXiv:0804.3318v1]. The nonresonant radiative field changes the effective shape of the target molecule by aligning it in the space-fixed frame. This markedly alters the differential and integral scattering cross sections. As the cross sections can be evaluated for a polarization of the radiative field collinear or perpendicular to the relative velocity vector, the model also offers predictions about steric asymmetry of the collisions.Comment: 19 pages, 7 figures, submitted to Int. J. Mass Spe

A Multiple Hypothesis Gaussian Mixture Filter for Acoustic Source Localization and Tracking

In this work, we address the problem of tracking an acoustic source based on measured time differences of arrival (TDOA). The classical solution to this problem consists in using a detector, which estimates the TDOA for each microphone pair, and then applying a tracking algorithm, which integrates the “measured” TDOAs in time. Such a two-stage approach presumes 1) that TDOAs can be estimated reliably; and 2) that the errors in detection behave in a well-defined fashion. The presence of noise and reverberation, however, causes larger errors in the TDOA estimates and, thereby, ultimately lowers the tracking performance. We propose to counteract this effect by considering a multiple hypothesis filter, which propagates the TDOA estimation uncertainty to the tracking stage. That is achieved by considering multiple TDOA estimates and then integrating the resulting TDOA observations in the framework of a Gaussian mixture filter. Experimental results show that the proposed filter has a significantly lower angular error than a multiple hypothesis particle filter

Statistische Signalverarbeitungsmethoden für Robuste Spracherkennung

Automatic speech recognition is becoming increasingly more important, with commercial applications such as call steering, dictation or voice-enabled personal assistance systems. Although successful in many respects, the performance of such systems can significantly degrade in noisy environment such as a crowded restaurant. This is due to the fact that noise introduces a mismatch between the clean speech features, which the ASR system has been trained with, and the noisy speech features that are encountered in the operational environment. This dissertation tries to mitigate the degradation in performance using two principally different approaches: speech feature enhancement (SFE) techniques, which minimize the mismatch between clean and noisy features, and missing feature reconstruction (MFR) techniques, which infer the values of noise-corrupted frequency bins from non-corrupted ones. Particular contributions include (1) a phase-averaged model of how noise corrupts clean speech features, (2) better noise estimation with a Monte Carlo variant of the expectation maximization algorithm, (3) an adaptive level of detail transform that allows for more accurate transformations of Gaussian random variables, and (4) a bounded conditional mean imputation technique. In addition to the above, it is shown that both SFE and MFR techniques can be derived within the same mathematical framework, just using different models of how noise corrupts clean speech features.Automatische Spracherkennung nimmt einen zusehends wichtigeren Stellenwert ein. Kommerzielle Anwendungen beinhalten Call Steering, Diktieren und sprachgesteuerte Assistenzsysteme. Obwohl derartige Anwendungen durchaus erfolgreich sein können, so leiden sie doch an der Tatsache, dass sich die Spracherkennungsgenauigkeit in geräuschbehafteten Umgebungen verschlechtert. Das rührt daher, dass Hintergrundgeräusche eine Unstimmigkeit zwischen klaren Sprachmerkmalen im Training und geräsuchbehafteten Merkmalen im Einsatz verursachen. Diese Dissertation untersucht zwei verschiedene Herangehensweisen an dieses Problem: Methoden zur Sprachmerkmalsverstärkung (SMV), welche Unstimmigkeiten zwischen Merkmalen minimieren, und Methoden zur Vervollständigung fehlender Merkmale (VFM), welche stark geräuschgestörte Frequenzen mittels weniger gestörter Frequenzen restaurieren. Spezifische Beiträge umfassen: (1) ein phasengemitteltes Modell dafür, wie Geräusche klare Sprachmerkmale korrumpieren, (2) verbesserte Geräuschschätzung durch einen Monte Carlo Expectation Maximization Algorithmus, (3) genauere Transformationen gaußscher Zufallsvariablen durch einen adaptiven Detailgrad, (4) eine Vervollständigungstechnik, die auf dem beschränkten, bedingten Mittelwert beruht. Zusätzlich zu obigem wird gezeigt, dass SMV und VFM Methoden sich im gleichen mathematischen Rahmenwerk herleiten lassen, nur eben unter Verwendung verschiedener Modelle für die Korrumpierung von Sprachmerkmalen

Increased Acceptance of Controller Assistance by Automatic Speech Recognition

Abstract—Situation awareness of today’s automation relies on sensor information, data bases and the information delivered by the operator using an appropriate HMI. The situation is mostly influenced by voice communications between controller and pilots. Hence, voice communication is an important part for the human operator to implement his plans. Voice communication runs independent and in parallel to the process the automation performs to understand the situation. Therefore, the automation, specifically the support system, is not aware of agreements between human operators. Even worse, the operators have additional effort to inform the support systems about their communication, i.e. their intents. This additional effort can be avoided by using automatic speech recognition systems (ASR). Nowadays, ASR is used in many applications, e.g. Siri® in Apple’s iPhone®

Considering uncertainty by particle filter enhanced speech features in large vocabulary continuous speech recognition

The goal of noise compensation techniques is the perfect reconstruction of clean features. Unfortunately, the reconstructed features can not be assumed to be perfect. Therefore, to improve performance, the uncertainty of enhanced speech features should be propagated into the hidden Markov model of automatic speech recognition systems. This paper shows how to jointly estimate the noise and the uncertainty (expressed by the variance) by particle filters in the logarithmic Mel power domain and how to propagate the uncertainty through the front-end into the hidden Markov model. In the experimental section, improvements in word accuracy of a large vocabulary continuous speech recognition system are presented. Index Terms — uncertainty of enhanced features, dynamic variance compensation, particle filter, noise robust automatic speech recognition, 1

An adaptive level of detail approach to nonlinear estimation

In this work, we present a general method for approximating nonlinear transformations of Gaussian mixture random variables. It is based on transforming the individual Gaussians with the unscented transform. The level of detail is adapted by iteratively splitting those components of the initial mixture that exhibited a high degree of nonlinearity during transformation. After each splitting operation, the affected components are re-transformed. This procedure gives more accurate results in cases where a Gaussian fit does not well represent the true distribution. Hence, it is of interest in a number of signal processing fields, ranging from nonlinear adaptive filtering to speech feature enhancement. In simulations, the proposed approach achieved a 48-fold reduction of the approximation error, compared to a single unscented transform

OVERCOMING THE VECTOR TAYLOR SERIES APPROXIMATION IN SPEECH FEATURE ENHANCEMENT —APARTICLE FILTER APPROACH

We present a simple, fast and previously unreported noise compensation method for particle filter (PF) based speech feature enhancement, which outperforms the vector Taylor series noise compensation method used by current PF approaches in terms of speed as well as word error rate. Furthermore, we devise a fast acceptance test that overcomes the particle decimation problem associated with PFs for speech feature enhancement, which makes the particle filter approach computationally more efficient. Index Terms — Speech feature enhancement, particle filter, vector Taylor series, statistical inference, automatic speech recognition 1

Coupling particle filters with automatic speech recognition for speech feature enhancement

This paper addresses robust speech feature extraction in combination with statistical speech feature enhancement and couples the particle filter to the speech recognition hypotheses. To extract noise robust features the Fourier transformation is replaced by the warped and scaled minimum variance distortionless response spectral envelope. To enhance the features, particle filtering has been used. Further, we show that the robust extraction and statistical enhancement can be combined to good effect. One of the critical aspects in particle filter design is the particle weight calculation which is traditionally based on a general, time independent speech model approximated by a Gaussian mixture distribution. We replace this general, time independent speech model by time- and phoneme-specific models. The knowledge of the phonemes to be used is obtained by the hypothesis of a speech recognition system, therefore establishing a coupling between the particle filter and the speech recognition system which have been treated as independent components in the past. Index Terms: particle filters, automatic speech recognition, speech feature enhancement, phoneme-specifi