Speaker segmentation and clustering

This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved

Evolutionary Speech Recognition

Automatic speech recognition systems are becoming ever more common and are increasingly deployed in more variable acoustic conditions, by very different speakers. So these systems, generally conceived in a laboratory, must be robust in order to provide optimal performance in real situations. This article explores the possibility of gaining robustness by designing speech recognition systems able to auto-modify in real time, in order to adapt to the changes of acoustic environment. As a starting point, the adaptive capacities of living organisms were considered in relation to their environment. Analogues of these mechanisms were then applied to automatic speech recognition systems. It appeared to be interesting to imagine a system adapting to the changing acoustic conditions in order to remain effective regardless of its conditions of use

The listening talker: A review of human and algorithmic context-induced modifications of speech

International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

Genetic algorithm application for electrodynamic transducer model identification

Research object: the adaptation and application of the genetic algorithm for electrodynamic transducer model parameters identification. Investigated problem: to formulate loudspeaker identification task as an optimization problem, adapt it to the genetic algorithm framework and compare obtained results with classical identification method using added mass. Main scientific results: the complete genetic algorithm loudspeaker identification procedure is presented, including: – data acquisition scheme, where the directly measured values for the algorithm application are: voltage at loudspeaker terminals, current through the voice coil and displacement of the moving part – selection of an appropriate set of genes of an individual – derivation of the fitness function for assessing the quality of the identified parameters, which can also be used to identify other types of electroacoustic transducers Also, the advantages of this method in comparison with the classical method of identification using added mass are considered, that are its versatility and ability to quickly configure and adapt for research and experimentation with different loudspeaker models and different types of transducers used in acoustics. Area of practical use of the research results: the proposed genetic loudspeaker model identification scheme can be directly applied on practice to speed up research and development tasks in electroacoustics and other related fields that require frequent experimentation with different types of transducer models. Innovative technological product: genetic algorithm based loudspeaker identification scheme that can be applied to identify various model of electrodynamic transducers. Scope of application of the innovative technological product: electroacoustics, loudspeaker design, audio system