Whisper-to-speech conversion using restricted Boltzmann machine arrays

Whispers are a natural vocal communication mechanism, in which vocal cords do not vibrate normally. Lack of glottal-induced pitch leads to low energy, and an inherent noise-like spectral distribution reduces intelligibility. Much research has been devoted to processing of whispers, including conversion of whispers to speech. Unfortunately, among several approaches, the best reconstructed speech to date still contains obviously artificial muffles and suffers from an unnatural prosody. To address these issues, the novel use of multiple restricted Boltzmann machines (RBMs) is reported as a statistical conversion model between whisper and speech spectral envelopes. Moreover, the accuracy of estimated pitch is improved using machine learning techniques for pitch estimation within only voiced (V) regions. Both objective and subjective evaluations show that this new method improves the quality of whisper-reconstructed speech compared with the state-of-the-art approaches

Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds

In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds. We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm. We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head. A non-linear dimensionality reduction technique is used to show that these data lie on a two-dimensional (2D) smooth manifold parameterized by the motor states of the listener, or equivalently, the sound source directions. We propose a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure. We derive a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound source direction. We extend this solution to deal with missing data and redundancy in real world spectrograms, and hence for 2D localization of natural sound sources such as speech. We further generalize the model to the challenging case of multiple sound sources and we propose a variational EM framework. The associated algorithm, referred to as variational EM for source separation and localization (VESSL) yields a Bayesian estimation of the 2D locations and time-frequency masks of all the sources. Comparisons of the proposed approach with several existing methods reveal that the combination of acoustic-space learning with Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table

A review of calibration methods for biometric systems in forensic applications

When, in a criminal case there are traces from a crime scene - e.g., finger marks or facial recordings from a surveillance camera - as well as a suspect, the judge has to accept either the hypothesis \emph{$H_{p}$} of the prosecution, stating that the trace originates from the subject, or the hypothesis of the defense \emph{$H_d$}, stating the opposite. The current practice is that forensic experts provide a degree of support for either of the two hypotheses, based on their examinations of the trace and reference data - e.g., fingerprints or photos - taken from the suspect. There is a growing interest in a more objective quantitative support for these hypotheses based on the output of biometric systems instead of manual comparison. However, the output of a score-based biometric system is not directly suitable for quantifying the evidential value contained in a trace. A suitable measure that is gradually becoming accepted in the forensic community is the Likelihood Ratio (LR) which is the ratio of the probability of evidence given \emph{$H_p$} and the probability of evidence given \emph{$H_d$}. In this paper we study and compare different score-to-LR conversion methods (called calibration methods). We include four methods in this comparative study: Kernel Density Estimation (KDE), Logistic Regression (Log Reg), Histogram Binning (HB), and Pool Adjacent Violators (PAV). Useful statistics such as mean and bias of the bootstrap distribution of \emph{LRs} for a single score value are calculated for each method varying population sizes and score location

Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning

Typically, voice conversion is regarded as an engineering problem with limited training data. The reliance on massive amounts of data hinders the practical applicability of deep learning approaches, which have been extensively researched in recent years. On the other hand, statistical methods are effective with limited data but have difficulties in modelling complex mapping functions. This paper proposes a voice conversion method that works with limited data and is based on stochastic variational deep kernel learning (SVDKL). At the same time, SVDKL enables the use of deep neural networks' expressive capability as well as the high flexibility of the Gaussian process as a Bayesian and non-parametric method. When the conventional kernel is combined with the deep neural network, it is possible to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem and, unlike the exact Gaussian process, allows for the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which considers both data fitting and model complexity. Considering the complexity of the model reduces the amount of training data by increasing the resistance to overfitting. To evaluate the proposed scheme, we examined the model's performance with approximately 80 seconds of training data. The results indicated that our method obtained a higher mean opinion score, smaller spectral distortion, and better preference tests than the compared methods

A hidden Markov model-based acoustic cicada detector for crowdsourced smartphone biodiversity monitoring

In recent years, the field of computational sustainability has striven to apply artificial intelligence techniques to solve ecological and environmental problems. In ecology, a key issue for the safeguarding of our planet is the monitoring of biodiversity. Automated acoustic recognition of species aims to provide a cost-effective method for biodiversity monitoring. This is particularly appealing for detecting endangered animals with a distinctive call, such as the New Forest cicada. To this end, we pursue a crowdsourcing approach, whereby the millions of visitors to the New Forest, where this insect was historically found, will help to monitor its presence by means of a smartphone app that can detect its mating call. Existing research in the field of acoustic insect detection has typically focused upon the classification of recordings collected from fixed field microphones. Such approaches segment a lengthy audio recording into individual segments of insect activity, which are independently classified using cepstral coefficients extracted from the recording as features. This paper reports on a contrasting approach, whereby we use crowdsourcing to collect recordings via a smartphone app, and present an immediate feedback to the users as to whether an insect has been found. Our classification approach does not remove silent parts of the recording via segmentation, but instead uses the temporal patterns throughout each recording to classify the insects present. We show that our approach can successfully discriminate between the call of the New Forest cicada and similar insects found in the New Forest, and is robust to common types of environment noise. A large scale trial deployment of our smartphone app collected over 6000 reports of insect activity from over 1000 users. Despite the cicada not having been rediscovered in the New Forest, the effectiveness of this approach was confirmed for both the detection algorithm, which successfully identified the same cicada through the app in countries where the same species is still present, and of the crowdsourcing methodology, which collected a vast number of recordings and involved thousands of contributors.</p

EVALUATION OF INTELLIGIBILITY AND SPEAKER SIMILARITY OF VOICE TRANSFORMATION

Voice transformation refers to a class of techniques that modify the voice characteristics either to conceal the identity or to mimic the voice characteristics of another speaker. Its applications include automatic dialogue replacement and voice generation for people with voice disorders. The diversity in applications makes evaluation of voice transformation a challenging task. The objective of this research is to propose a framework to evaluate intentional voice transformation techniques. Our proposed framework is based on two fundamental qualities: intelligibility and speaker similarity. Intelligibility refers to the clarity of the speech content after voice transformation and speaker similarity measures how well the modified output disguises the source speaker. We measure intelligibility with word error rates and speaker similarity with likelihood of identifying the correct speaker. The novelty of our approach is, we consider whether similarly transformed training data are available to the recognizer. We have demonstrated that this factor plays a significant role in intelligibility and speaker similarity for both human testers and automated recognizers. We thoroughly test two classes of voice transformation techniques: pitch distortion and voice conversion, using our proposed framework. We apply our results for patients with voice hypertension using video self-modeling and preliminary results are presented