Little research has been conducted to gauge a listener's ability to recognise or identify speakers when presented with samples of singing within the field of Forensic Speech Science.Eight friends and two foil speakers were recorded speaking and singing to investigate the effects of speaker familiarity and singing in speaker identification tasks.The stimuli were used to create a listening test completed by close social network speakers, members of the wider social network, and general lay listeners.The study aimed to explore the impact of familiarity on an individual's ability to recognise speakers when presented with spoken and sung stimuli.The results revealed that the listeners within the close social network were the most successful in the listening test.Overall, listeners performed best when both samples were spoken, however, those in the close social network were less affected by the use of sung samples, and scored higher, compared to those outside the close social network.

Articulatory speech synthesis has the potential to offer more natural sounding synthetic speech than established concatenative or parametric synthesis methods. Time-domain acoustic models are particularly suited to the dynamic nature of the speech signal, and recent work has demonstrated the potential of dynamic vocal tract models that accurately reproduce the vocal tract geometry. This paper presents a dynamic 3D digital waveguide mesh (DWM) vocal tract model, capable of movement to produce diphthongs. The technique is compared to existing dynamic 2D and static 3D DWM models, for both monophthongs and diphthongs. The results indicate that the proposed model provides improved formant accuracy over existing DWM vocal tract models. Furthermore, the computational requirements of the proposed method are significantly lower than those of comparable dynamic simulation techniques. This paper represents another step toward a fully functional articulatory vocal tract model which will lead to more natural speech synthesis systems for use across society.

Following recent advances in direct modeling of the speech waveform using a deep neural network, we propose a novel method that directly estimates a physical model of the vocal tract from the speech waveform, rather than magnetic resonance imaging data. This provides a clear relationship between the model and the size and shape of the vocal tract, offering considerable flexibility in terms of speech characteristics such as age and gender. Initial tests indicate that despite a highly simplified physical model, intelligible synthesized speech is obtained. This illustrates the potential of the combined technique for the control of physical models in general, and hence the generation of more natural-sounding synthetic speech.

This paper describes a novel technique for promoting sparsity in the modified filtered-x algorithms required for active noise control. The proposed algorithms are based on recent techniques incorporating approximations to the l0-norm in the cost functions that are used to derive adaptive filtering algorithms. In particular, zero-attracting and reweighted zero-attracting filtered-x adaptive algorithms are developed and considered for active noise control problems. The results of simulations indicate that the proposed techniques improve the convergence of the existing modified algorithm in the case where the primary and secondary paths exhibit a degree of sparsity.