Analysis of the effects of jitter, relative intensity noise, and nonlinearity on a photonic digital-to-analog converter based on optical Nyquist pulse synthesis - Kress, Christian; Bahmanian, Meysam; Schwabe, Tobias; Scheytt, J. Christoph (Opt. Express)
An analysis of an optical Nyquist pulse synthesizer using Mach-Zehnder modulators is presented. The analysis allows to predict the upper limit of the effective number of bits of this type of photonic digital-to-analog converter. The analytical solution has been verified by means of electro-optic simulations. With this analysis the limiting factor for certain scenarios: relative intensity noise, distortions by driving the Mach-Zehnder modulator, or the signal generator phase noise can quickly be identified.
Keywords: Analog to digital converters; Diode lasers; Laser sources; Phase noise; Signal processing; Wavelength division multiplexers

On Optimal Smoothing in Minimum Statistics Based Noise Tracking - Chinaev, Aleksej; Haeb-Umbach, Reinhold (Interspeech 2015)
Noise tracking is an important component of speech enhancement algorithms. Of the many noise trackers proposed, Minimum Statistics (MS) is a particularly popular one due to its simple parameterization and at the same time excellent performance. In this paper we propose to further reduce the number of MS parameters by giving an alternative derivation of an optimal smoothing constant. At the same time the noise tracking performance is improved as is demonstrated by experiments employing speech degraded by various noise types and at different SNR values.
Keywords: speech enhancement; noise tracking; optimal smoothing

Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions - Heymann, Jahn; Haeb-Umbach, Reinhold; Golik, P.; Schlueter, R. (Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on)
The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data.
Keywords: codecs; signal denoising; speech recognition; Bayesian feature enhancement; denoising autoencoder; reverberant ASR; single-channel speech recognition; speaker to microphone distances; unsupervised adaptation; Adaptation models; Noise reduction; Reverberation; Speech; Speech recognition; Training; deep neuronal networks; denoising autoencoder; feature enhancement; robust speech recognition

A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE/ACM Transactions on Audio, Speech, and Language Processing)
In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.
Keywords: computational complexity; reverberation; speech recognition; automatic speech recognition; background noise; clean speech; computational complexity; energy compensation; logarithmic mel power spectral domain; mel frequency cepstral coefficients; microphone input signals; model-based feature compensation schemes; noisy reverberant speech automatic recognition; noisy reverberant speech features; reverberation; Atmospheric modeling; Computational modeling; Noise; Noise measurement; Reverberation; Speech; Vectors; Model-based feature compensation; observation model for reverberant and noisy speech; recursive observation model; robust automatic speech recognition

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

GMM-based significance decoding - Abdelaziz, Ahmed H.; Zeiler, Steffen; Kolossa, Dorothea; Leutnant, Volker; Haeb-Umbach, Reinhold (Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on)
The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios.
Keywords: Bayes methods; Gaussian processes; convolution; decision theory; decoding; noise; reverberation; speech coding; speech recognition; Bayesian decision rule; GMM; Gaussian mixture models; additive noise scenarios; automatic speech recognition systems; convolutive noise scenarios; decoding approach; mathematical framework; reverberant environments; significance decoding; speech feature estimation; uncertainty-of-observation techniques; Hidden Markov models; Maximum likelihood decoding; Noise; Speech; Speech recognition; Uncertainty; Uncertainty-of-observation; modified imputation; noise robust speech recognition; significance decoding; uncertainty decoding

MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations - Chinaev, Aleksej; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.
Keywords: Gaussian noise; maximum likelihood estimation; parameter estimation; GMM parameter; Gaussian mixture model; MAP estimation; Map-based estimation; maximum a-posteriori estimation; maximum likelihood technique; noisy observation; sequential estimation framework; white Gaussian noise; Additive noise; Gaussian mixture model; Maximum likelihood estimation; Noise measurement; Gaussian mixture model; Maximum a posteriori estimation; Maximum likelihood estimation

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.
Keywords: Bayes methods; compensation; error statistics; reverberation; speech recognition; Bayesian feature enhancement; background noise; clean speech feature vectors; compensation; connected digits recognition task; error statistics; memory requirements; noisy reverberant data; posteriori probability density function; recursive formulation; reverberant logarithmic mel power spectral coefficients; robust automatic speech recognition; signal-to-noise ratios; time-variant observation; word error rate reduction; Robust automatic speech recognition; model-based Bayesian feature enhancement; observation model for reverberant and noisy speech; recursive observation model

Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation - Vu, Dang Hai Tran; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm.
Keywords: correlation methods; estimation theory; hidden Markov models; iterative methods; probability; spectral analysis; speech processing; 2D HMM; SPP estimates; iterative algorithm; posterior probability estimation; spectral correlation; speech presence probability estimation; state-of-the-art SPP estimation algorithm; temporal correlation; turbo principle; two-dimensional hidden Markov model; Correlation; Decoding; Estimation; Iterative decoding; Noise; Speech; Vectors

Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor - Chinaev, Aleksej; Krueger, Alexander; Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012))
In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores.
Keywords: MAP parameter estimation; noise power estimation; speech enhancement