Speech technologies have been developed for decades as a typical signal processing area, while the last decade has brought a huge progress based on new machine learning paradigms. Owing not only to their intrinsic complexity but also to their relation with cognitive sciences, speech technologies are now viewed as a prime example of interdisciplinary knowledge area. This review article on speech signal analysis and processing, corresponding machine learning algorithms, and applied computational intelligence aims to give an insight into several fields, covering speech production and auditory perception, cognitive aspects of speech communication and language understanding, both speech recognition and text-to-speech synthesis in more details, and consequently the main directions in development of spoken dialogue systems. Additionally, the article discusses the concepts and recent advances in speech signal compression, coding, and transmission, including cognitive speech coding. To conclude, the main intention of this article is to highlight recent achievements and challenges based on new machine learning paradigms that, over the last decade, had an immense impact in the field of speech signal processing.

An optimized vector-quantization-inspired signal constellation design (OVQ-SCD) suitable for multidimensional optical transport is proposed, in which signal constellation radii transformation function is optimized and near-uniform distribution of points is achieved. The proposed OVQ-SCD is used in a tandem with a hybrid multidimensional coded-modulation scheme employing Slepian sequences as electrical discrete-time basis functions, orthogonal prolate spheroidal wave functions as impulse responses of optical filters in orthogonal-division multiplexing, and spatial modes as optical continuous-time basis functions. It has been shown that the proposed multidimensional coded-modulation schemes based on OVQ-SCDs outperform corresponding counterparts and can be used to enable beyond 10 Pb/s serial optical transport over spatial division multiplexing (SDM) fibers as well as beyond 1 Pb/s transport over SMFs. The proposed OVQ-SCD-based hybrid multidimensional coded modulation scheme can simultaneously solve the problems related to the limited bandwidth of information-infrastructure, high energy consumption, and heterogeneity of network segments; while enabling elastic and dynamic bandwidth allocation.

Speaker recognition is an important classification task, which can be solved using several approaches. Although building a speaker recognition model on a closed set of speakers under neutral speaking conditions is a well-researched task and there are solutions that provide excellent performance, the classification accuracy of developed models significantly decreases when applying them to emotional speech or in the presence of interference. Furthermore, deep models may require a large number of parameters, so constrained solutions are desirable in order to implement them on edge devices in the Internet of Things systems for real-time detection. The aim of this paper is to propose a simple and constrained convolutional neural network for speaker recognition tasks and to examine its robustness for recognition in emotional speech conditions. We examine three quantization methods for developing a constrained network: floating-point eight format, ternary scalar quantization, and binary scalar quantization. The results are demonstrated on the recently recorded SEAC dataset.

An efficient ECG modeling algorithm is presented in this paper. The model is based on fitting polynomial functions to real ECG. This algorithm describes a segmentation of a heartbeat and fitting an appropriate polynomial function to the segments. The model performances are evaluated in terms of PRD, preserve of ST-T segment clinical information and the execution time. When comparing this model with the existing one, the PRD improvements can be seen, especially in those signals with high morphological diversity heartbeats. Moreover, the computing time is significantly reduced. Using appropriate model features, ST-T analysis achieves an average accuracy of more than 94%. The obtained data shows that this model is applicable to other ECG processing like: heartbeat analysis, compression and filtering. Ill. 2, bibl. 9, tabl. 3 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.110.4.304