Effects of acoustic features modifications on the perception of dysarthric speech - preliminary study (pitch, intensity and duration modifications)

Marking stress is important in conveying meaning and drawing listener’s attention to specific parts of a message. Extensive research has shown that healthy speakers mark stress using three main acoustic cues; pitch, intensity, and duration. The relationship between acoustic and perception cues is vital in the development of a computer-based tool that aids the therapists in providing effective treatment to people with Dysarthria. It is, therefore, important to investigate the acoustic cues deficiency in dysarthric speech and the potential compensatory techniques needed for effective treatment. In this paper, the relationship between acoustic and perceptive cues in dysarthric speech are investigated. This is achieved by modifying stress marked sentences from 10 speakers with Ataxic dysarthria. Each speaker produced 30 sentences using the 10 Subject-Verb-Object-Adjective (SVOA) structured sentences across three stress conditions. These stress conditions are stress on the initial (S), medial (O) and final (A) target words respectively. To effectively measure the deficiencies in Dysarthria speech, the acoustic features (pitch, intensity, and duration) are modified incrementally. The paper presents the techniques involved in the modification of these acoustic features. The effects of these modifications are analysed based on steps of 25% increments in pitch, intensity and duration. For robustness and validation, 50 untrained listeners participated in the listening experiment. The results and the relationship between acoustic modifications (what is measured) and perception (what is heard) in Dysarthric speech are discussed

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

Linear Gaussian State-Space Models are widely used and a Bayesian treatment of parameters is therefore of considerable interest. The approximate Variational Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics to bioinformatics. The most challenging aspect of implementing the method is in performing inference on the hidden state sequence of the model. We show how to convert the inference problem so that standard and stable Kalman Filtering/Smoothing recursions from the literature may be applied. This is in contrast to previously published approaches based on Belief Propagation. Our framework both simplifies and unifies the inference problem, so that future applications may be easily developed. We demonstrate the elegance of the approach on Bayesian temporal ICA, with an application to finding independent components in noisy EEG signals

Bayesian Interpolation and Parameter Estimation in a Dynamic Sinusoidal Model

In this paper, we propose a method for restoring the missing or corrupted observations of nonstationary sinusoidal signals which are often encountered in music and speech applications. To model nonstationary signals, we use a time-varying sinusoidal model which is obtained by extending the static sinusoidal model into a dynamic sinusoidal model. In this model, the in-phase and quadrature components of the sinusoids are modeled as first-order Gauss–Markov processes. The inference scheme for the model parameters and missing observations is formulated in a Bayesian framework and is based on a Markov chain Monte Carlo method known as Gibbs sampler. We focus on the parameter estimation in the dynamic sinusoidal model since this constitutes the core of model-based interpolation. In the simulations, we first investigate the applicability of the model and then demonstrate the inference scheme by applying it to the restoration of lost audio packets on a packet-based network. The results show that the proposed method is a reasonable inference scheme for estimating unknown signal parameters and interpolating gaps consisting of missing/corrupted signal segments

Statistical models for natural sounds

It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds