Blind Normalization of Speech From Different Channels

We show how to construct a channel-independent representation of speech that has propagated through a noisy reverberant channel. This is done by blindly rescaling the cepstral time series by a non-linear function, with the form of this scale function being determined by previously encountered cepstra from that channel. The rescaled form of the time series is an invariant property of it in the following sense: it is unaffected if the time series is transformed by any time-independent invertible distortion. Because a linear channel with stationary noise and impulse response transforms cepstra in this way, the new technique can be used to remove the channel dependence of a cepstral time series. In experiments, the method achieved greater channel-independence than cepstral mean normalization, and it was comparable to the combination of cepstral mean normalization and spectral subtraction, despite the fact that no measurements of channel noise or reverberations were required (unlike spectral subtraction).Comment: 25 pages, 7 figure

On the Distribution of Speaker Verification Scores: Generative Models for Unsupervised Calibration

Speaker verification systems whose outputs can be interpreted as log-likelihood ratios (LLR) allow for cost-effective decisions by comparing the system outputs to application-defined thresholds depending only on prior information. Classifiers often produce uncalibrated scores, and require additional processing to produce well-calibrated LLRs. Recently, generative score calibration models have been proposed, which achieve calibration performance close to that of state-of-the-art discriminative techniques for supervised scenarios, while also allowing for unsupervised training. The effectiveness of these methods, however, strongly depends on their capabilities to correctly model the target and non-target score distributions. In this work we propose theoretically grounded and accurate models for characterizing the distribution of scores of speaker verification systems. Our approach is based on tied Generalized Hyperbolic distributions and overcomes many limitations of Gaussian models. Experimental results on different NIST benchmarks, using different utterance representation front-ends and different back-end classifiers, show that our method is effective not only in supervised scenarios, but also in unsupervised tasks characterized by very low proportion of target trials

Polyphonic Sound Event Detection by using Capsule Neural Networks

Artificial sound event detection (SED) has the aim to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, Deep Learning offers valuable techniques for this goal such as Convolutional Neural Networks (CNNs). The Capsule Neural Network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e., perspective, size, orientation) and the detection of overlapped images. This motivated the authors to employ CapsNets to deal with the polyphonic-SED task, in which multiple sound events occur simultaneously. Specifically, we propose to exploit the capsule units to represent a set of distinctive properties for each individual sound event. Capsule units are connected through a so-called "dynamic routing" that encourages learning part-whole relationships and improves the detection performance in a polyphonic context. This paper reports extensive evaluations carried out on three publicly available datasets, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also allows to achieve the best results with respect to the state of the art algorithms