This paper investigates the problem of speaker identification and verification in noisy conditions, assuming that speech signals are corrupted by environmental noise, but knowledge about the noise characteristics is not available. This research is motivated in part by the potential application of speaker recognition technologies on handheld devices or the Internet. While the technologies promise an additional biometric layer of security to protect the user, the practical implementation of such systems faces many challenges. One of these is environmental noise. Due to the mobile nature of such systems, the noise sources can be highly time-varying and potentially unknown. This raises the requirement for noise robustness in the absence of information about the noise. This paper describes a method that combines multicondition model training and missing-feature theory to model noise with unknown temporal-spectral characteristics. Multicondition training is conducted using simulated noisy data with limited noise variation, providing a ldquocoarserdquo compensation for the noise, and missing-feature theory is applied to refine the compensation by ignoring noise variation outside the given training conditions, thereby reducing the training and testing mismatch. This paper is focused on several issues relating to the implementation of the new model for real-world applications. These include the generation of multicondition training data to model noisy speech, the combination of different training data to optimize the recognition performance, and the reduction of the model's complexity. The new algorithm was tested using two databases with simulated and realistic noisy speech data. The first database is a redevelopment of the TIMIT database by rerecording the data in the presence of various noise types, used to test the model for speaker identification with a focus on the varieties of noise. The second database is a handheld-device database collected in realistic noisy conditions, used to further validate the model for real-world speaker verification. The new model is compared to baseline systems and is found to achieve lower error rates.

/pdf/robust-speaker-recognition-in-noisy-conditions-27hhi7puif.pdf

Robust Speaker Recognition in Noisy Conditions

Speech recognition in noisy environments

The enhancement of speech degraded by real-world interferers is a highly relevant and difficult task. Its importance arises from the multitude of practical applications, whereas the difficulty is due to the fact that interferers are often nonstationary and potentially similar to speech. The goal of monaural speech enhancement is to separate a single mixture into its underlying clean speech and interferer components. This under-determined problem is solved by incorporating prior knowledge in the form of learned speech and interferer dictionaries. The clean speech is recovered from the degraded speech by sparse coding of the mixture in a composite dictionary consisting of the concatenation of a speech and an interferer dictionary. Enhancement performance is measured using objective measures and is limited by two effects. A too sparse coding of the mixture causes the speech component to be explained with too few speech dictionary atoms, which induces an approximation error we denote source distortion. However, a too dense coding of the mixture results in source confusion, where parts of the speech component are explained by interferer dictionary atoms and vice-versa. Our method enables the control of the source distortion and source confusion trade-off, and therefore achieves superior performance compared to powerful approaches like geometric spectral subtraction and codebook-based filtering, for a number of challenging interferer classes such as speech babble and wind noise.

/pdf/speech-enhancement-using-generative-dictionary-learning-3vecc5kft3.pdf

Speech Enhancement Using Generative Dictionary Learning

This paper brings together two important aspects of the human-machine interaction in cars: the psychological aspect and the engineering aspect. The psychologically motivated part of this study addresses questions such as whyit is important to automatically assess the driver's affective state, which states are important and how a machine's response should look like. The engineering part studies howthe emotional state of a driver can be estimated by extracting acoustic features from the speech signal and mapping them to an emotion state in a multidimensional, continuous-valued emotion space. Such a feasibility study is performed in an experiment in which spontaneous, authentic emotional utterances are superimposed by car noise of several car types and various road surfaces.

http://www.grimmm.de/research/Publications/Grimm07%20On%20the%20Necessity%20and%20Feasibility%20of%20Detecting%20a%20Drivers%20Emotional%20State%20While%20Driving%20%5BACII-AuthorVersion%5D.pdf

On the Necessity and Feasibility of Detecting a Driver's Emotional State While Driving

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.

A Data-Driven Approach to A Priori SNR Estimation

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.

Environment-Optimized Speech Enhancement

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

Quality Assessment of Speech Enhancement Systems by Separation of Enhanced Speech, Noise, and Echo

The performance of speaker verification (SV) systems degrades rapidly in noise rendering them unsuitable for security-critical applications in mobile phones, where false acceptance rates (FAR) of ∼ 10−4 are required. However, less demanding applications for which equal error rates (EER) comparable to word error rates (WER) of speech recognizers are acceptable could benefit from the SV technology. In this paper we evaluate two feature-based noise compensation algorithms in the context of SV: vector Taylor series (VTS) combined with statistical linear approximation (SLA), and Kalman filter-based interacting multiple models (IMM). Tests with the YOHO database and the NTT-AT ambient noises show that EERs as low as 5%–10% in medium to high noise conditions can be achieved for a text-independent SV system.

An evaluation of VTS and IMM for speaker verification in noise.

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.

Suhadi Suhadi

Papers

A Data-Driven Approach to A Priori SNR Estimation

Environment-Optimized Speech Enhancement

Quality Assessment of Speech Enhancement Systems by Separation of Enhanced Speech, Noise, and Echo

An evaluation of VTS and IMM for speaker verification in noise.

Towards objective quality assessment of speech enhancement systems in a black box approach