Writing qualitative IS literature reviews ? Guidelines for synthesis, interpretation and guidance of research - Schryen, Guido (Communications of the AIS)

Spectral Noise Tracking for Improved Nonstationary Noise Robust ASR - Chinaev, Aleksej; Puels, Marc; Haeb-Umbach, Reinhold (11. ITG Fachtagung Sprachkommunikation (ITG 2014))
"A method for nonstationary noise robust automatic speech recognition (ASR) is to first estimate the changing noise statistics and second clean up the features prior to recognition accordingly. Here, the first is accomplished by noise tracking in the spectral domain, while the second relies on Bayesian enhancement in the feature domain. In this way we take advantage of our recently proposed maximum a-posteriori based (MAP-B) noise power spectral density estimation algorithm, which is able to estimate the noise statistics even in time-frequency bins dominated by speech. We show that MAP-B noise tracking leads to an improved noise model estimate in the feature domain compared to estimating noise in speech absence periods only, if the bias resulting from the nonlinear transformation from the spectral to the feature domain is accounted for. Consequently, ASR results are improved, as is shown by experiments conducted on the Aurora IV database."

Source Counting in Speech Mixtures Using a Variational EM Approach for Complexwatson Mixture Models - Drude, Lukas; Chinaev, Aleksej; Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014))
"In this contribution we derive a variational EM (VEM) algorithm for model selection in complex Watson mixture models, which have been recently proposed as a model of the distribution of normalized microphone array signals in the short-time Fourier transform domain. The VEM algorithm is applied to count the number of active sources in a speech mixture by iteratively estimating the mode vectors of the Watson distributions and suppressing the signals from the corresponding directions. A key theoretical contribution is the derivation of the MMSE estimate of a quadratic form involving the mode vector of the Watson distribution. The experimental results demonstrate the effectiveness of the source counting approach at moderately low SNR. It is further shown that the VEM algorithm is more robust w.r.t. used threshold values."

Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models - Drude, Lukas; Chinaev, Aleksej; Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014))
This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data.
Keywords: Accuracy; Acoustics; Estimation; Mathematical model; Soruce separation; Speech; Vectors; Bayes methods; Blind source separation; Directional statistics; Number of speakers; Speaker diarization

Iterative Bayesian Word Segmentation for Unspuervised Vocabulary Discovery from Phoneme Lattices - Heymann, Jahn; Walter, Oliver; Haeb-Umbach, Reinhold; Raj, Bhiksha (39th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014))
"In this paper we present an algorithm for the unsupervised segmentation of a lattice produced by a phoneme recognizer into words. Using a lattice rather than a single phoneme string accounts for the uncertainty of the recognizer about the true label sequence. An example application is the discovery of lexical units from the output of an error-prone phoneme recognizer in a zero-resource setting, where neither the lexicon nor the language model (LM) is known. We propose a computationally efficient iterative approach, which alternates between the following two steps: First, the most probable string is extracted from the lattice using a phoneme LM learned on the segmentation result of the previous iteration. Second, word segmentation is performed on the extracted string using a word and phoneme LM which is learned alongside the new segmentation. We present results on lattices produced by a phoneme recognizer on the WSJCAM0 dataset. We show that our approach delivers superior segmentation performance than an earlier approach found in the literature, in particular for higher-order language models. "

Coordinate Mapping Between an Acoustic and Visual Sensor Network in the Shape Domain for a Joint Self-Calibrating Speaker Tracking - Jacob, Florian; Haeb-Umbach, Reinhold (11. ITG Fachtagung Sprachkommunikation (ITG 2014))
"Several self-localization algorithms have been proposed, that determine the positions of either acoustic or visual sensors autonomously. Usually these positions are given in a modality specific coordinate system, with an unknown rotation, translation and scale between the different systems. For a joint audiovisual tracking, where the different modalities support each other, the two modalities need to be mapped into a common coordinate system. In this paper we propose to estimate this mapping based on audiovisual correlates, i.e., a speaker that can be localized by both, a microphone and a camera network separately. The voice is tracked by a microphone network, which had to be calibrated by a self-localization algorithm at first, and the head is tracked by a calibrated camera network. Unlike existing Singular Value Decomposition based approaches to estimate the coordinate system mapping, we propose to perform an estimation in the shape domain, which turns out to be computationally more efficient. Simulations of the self-localization of an acoustic sensor network and a following coordinate mapping for a joint speaker localization showed a significant improvement of the localization performance, since the modalities were able to support each other."

An Overview of Noise-Robust Automatic Speech Recognition - Li, Jinyu; Deng, Li; Gong, Yifan; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech and Language Processing)
New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.
Keywords: Speech recognition; compensation; distortion modeling; joint model training; noise; robustness; uncertainty processing

An Evaluation of Unsupervised Acoustic Model Training for a Dysarthric Speech Interface - Walter, Oliver; Despotovic, Vladimir; Haeb-Umbach, Reinhold; Gemmeke, Jrt; Ons, Bart; Van hamme, Hugo (INTERSPEECH 2014)
In this paper, we investigate unsupervised acoustic model training approaches for dysarthric-speech recognition. These models are first, frame-based Gaussian posteriorgrams, obtained from Vector Quantization (VQ), second, so-called Acoustic Unit Descriptors (AUDs), which are hidden Markov models of phone-like units, that are trained in an unsupervised fashion, and, third, posteriorgrams computed on the AUDs. Experiments were carried out on a database collected from a home automation task and containing nine speakers, of which seven are considered to utter dysarthric speech. All unsupervised modeling approaches delivered significantly better recognition rates than a speaker-independent phoneme recognition baseline, showing the suitability of unsupervised acoustic model training for dysarthric speech. While the AUD models led to the most compact representation of an utterance for the subsequent semantic inference stage, posteriorgram-based representations resulted in higher recognition rates, with the Gaussian posteriorgram achieving the highest slot filling F-score of 97.02%. Index Terms: unsupervised learning, acoustic unit descriptors, dysarthric speech, non-negative matrix factorization

Revisiting SSL/TLS Implementations: New Bleichenbacher Side Channels and Attacks - Meyer, Christopher; Somorovsky, Juraj; Weiss, Eugen; Schwenk, Jörg; Schinzel, Sebastian; Tews, Erik (23rd USENIX Security Symposium (USENIX Security 14))

Writing: Movements of the self - Karsten, Andrea (Theory & Psychology)
This article presents analyses of excerpts from a study on writing conducted in a dialogical perspective. The study’s material was collected by the auto-confrontation method: writers were videotaped during their work and afterwards confronted with their writing activities. Microanalysis of the material attends to how inner dialogues during writing are “refracted” (Voloshinov) in auto-confrontation. Bakhtin’s notion of the chronotope (time-and-space) as the main tool of analysis helps to discern the changing contexts and position constellations utterances are valid for. It thus sheds light on the positioning movements performed by the writing selves through language. The analyses show various utterance movements traversing the chronotopes involved, ranging from refractions of movements between the writers’ inner dialogues and their texts to retrospective imperatives with a developmental potential. This “dialogical volume” of speech activity presenting itself in writing can contribute to our understanding of the interplay of language and the self.
Keywords: auto-confrontation; chronotope; inner dialogue; microanalysis; positioning; writing