The a priori signal-to-noise ratio (SNR) plays an important role in many speech enhancement algorithms. In this paper, we present a data-driven approach to a priori SNR estimation. It may be used with a wide range of speech enhancement techniques, such as, e.g., the minimum mean square error (MMSE) (log) spectral amplitude estimator, the super Gaussian joint maximum a posteriori (JMAP) estimator, or the Wiener filter. The proposed SNR estimator employs two trained artificial neural networks, one for speech presence, one for speech absence. The classical decision-directed a priori SNR estimator by Ephraim and Malah is broken down into its two additive components, which now represent the two input signals to the neural networks. Both output nodes are combined to represent the new a priori SNR estimate. As an alternative to the neural networks, also simple lookup tables are investigated. Employment of these data-driven nonlinear a priori SNR estimators reduces speech distortion, particularly in speech onset, while retaining a high level of noise attenuation in speech absence.

Quality assessment of speech enhancement systems has to deal with aspects such as distortion of the near-end talker's speech, and with the attenuation and distortion of the noise and the echo in different test cases. We propose first steps into the direction of a new black box objective quality assessment of speech enhancement schemes, based on our previous work on decomposition of the (enhanced) speech signal into its components speech, (residual) noise, and (residual) echo. Having these signals available, to our knowledge, for the first time a black box objective quality assessment of an entire speech enhancement system is proposed allowing for simultaneous measurement of, e.g., noise attenuation, echo return loss enhancement (ERLE), and perceptual evaluation of speech quality (PESQ) of the speech component in a wide range of test scenarios including double-talk. The derived scheme proves to be very useful for testing hands-free devices in practice but also for objective evaluation of sophisticated algorithms in science.

In this paper, we present a training-based approach to speech enhancement that exploits the spectral statistical characteristics of clean speech and noise in a specific environment. In contrast to many state-of-the-art approaches, we do not model the probability density function (pdf) of the clean speech and the noise spectra. Instead, subband-individual weighting rules for noisy speech spectral amplitudes are separately trained for speech presence and speech absence from noise recordings in the environment of interest. Weighting rules for a variety of cost functions are given; they are parameterized and stored as a table look-up. The speech enhancement system simply works by computing the weighting rules from the table look-up indexed by the a posteriori signal-to-noise ratio (SNR) and the a priori SNR for each subband computed on a Bark scale. Optimized for an automotive environment, our approach outperforms known-environment-independent-speech enhancement techniques, namely the a priori SNR-driven Wiener filter and the minimum mean square error (MMSE) log-spectral amplitude estimator, both in terms of speech distortion and noise attenuation.

Abstract Quality assessment of speech enhancement systems is a non-trivial task, especially when (residual) noise and echo signalcomponents occur. We present a signal separation schemethat allows for a detailed analysis of unknown speech enhance-ment systems in a black box test scenario. Our approach sep-arates the speech, (residual) noise, and (residual) echo compo-nent of the speech enhancement system in the sending direc-tion (uplink direction). This makes it possible to independentlyjudge the speech degradation and the noise and echo attenua-tion/degradation. While state of the art tests always try to judgethe sending direction signal mixture, our new scheme allows amore reliable analysis in shorter time. It will be very usefulfor testing hands-free devices in practice as well as for testingspeech enhancement algorithms in research and development. Index Terms : objective signal quality assessment, non-blindsignal separation, speech enhancement, hands-free 1. Introduction In science, a comfortable way to evaluate speech enhancementalgorithms is to digitally add near-end speech and noise to theecho signal and thereby construct the microphone signal. Dur-ing the uplink processing of the speech enhancement (hands-free) system the operational influence on the noisy microphonesignal is then to be logged, and later applied individually to thespeech, echo, and noise components of the microphone signal(see, e.g., [1, 2, 3]). This presumes linear processing, as canbe found e.g. in frequency domain noise reduction, where again is applied to the spectral amplitudes. The strength of suchmethod is that one achieves three separate signals: The filteredspeechcomponent, thefilteredechocomponent, andthefilterednoise component, which represent the (slightly) distorted near-end talker’s speech signal, the suppressed echo signal, and theresidual noisesignal, respectively. Focusingonnoisereduction,e.g., aspects such as speech distortion, noise attenuation, andnoisedistortioncan thencomfortably bemeasured or auditivelyassessed.Thishowever isa

In this paper, we give a solution on how to overcome the assumption of statistical independence of adjacent frequency bins in noise reduction techniques. We show that under relaxed assumptions the problem results in an a-priori SNR estimation problem, where all available noisy speech spectral amplitudes (observations) are exploited. Any state-of-the-art noise power spectral density (psd) estimation and weighting rule can be used - they do not need to be restated. In order to solve for an estimator well suited for real-time applications, we model the a-priori SNR values as Markov processes w.r.t. frequency. On the basis of the formulation by Ephraim and Malah, this leads to a new a-priori SNR estimator that yields fewer musical tones.