At this stage, particular attention was paid to determining procedures for integrating the virtual 3D hexapod model with the results of calculations performed in the LabVIEW. The next stage of work included implementing the mathematical model describing the functioning of a hexapod in the LabVIEW software. Then were defined the constraints of the "joint" type (e.g.: revolute joint, slider joint, spherical joint) between the created component of the "link" type, so that the computer simulation corresponds to the operation of a real hexapod. In the model prepared for movement simulation were created links corresponding to such elements as: electric actuator, top plate, bottom plate, ball-and-socket joint, toggle joint Phillips. Individual links were defined according to the nature of the hexapod elements action. The first step was to define the components of the 3D model in the form of "links". This phase of the work was done in the "Motion Simulation" module of the CAD/CAE/CAM Siemens NX system. In the first stage of the work concerning the integration task the 3D model to simulate movements of a hexapod was elaborated. The purpose of the integration is to determine the workspace of a hexapod model basing on a mathematical model describing it motion. The paper presents the problems related to the integration of a CAD/CAE system with the LabVIEW software. Finally, the speech recognition and RFID systems were achieved in an actual environment to prove its feasibility and stability, and implemented into the omni-directional mobile robot. After processing by the same reference model and comparing with previous reference model, the path of the maximum total probability in various models found using the Viterbi algorithm in the recognition was the recognition result. The trained reference model was put into the industrial computer on the robot platform, and the user entered the isolated words to be tested. Then, the Hidden Markov Model (HMM) was used for model training of the speech database, and the Viterbi algorithm was used to find an optimal state sequence as the reference sample for speech recognition. After the speech pre-processing of this speech database, the feature parameters of cepstrum and delta-cepstrum were obtained using linear predictive coefficient (LPC). The speaker first recorded the isolated words for the robot to create speech database of specific speakers. For speech recognition, the speech signals were captured by short-time processing. This paper applied speech recognition and RFID technologies to develop an omni-directional mobile robot into a robot with voice control and guide introduction functions. Praaline is free software, released under the GPL license. A series of visualisations, editors and plug-ins are provided. Praaline is extensible using Python or C++ plug-ins, while Praat and R scripts may be executed against the corpus data. The corpus database may be queried, to produce aggregated data-sets. Users may run a customisable cascade of analysis steps, based on plug-ins and scripts, and update the database with the results. Corpus metadata and annotations may be stored in a database, locally or remotely, and users can define the metadata and annotation structure. ![]() Users are exposed to an integrated, user-friendly interface from which to access multiple tools. Praaline integrates and extends existing time-proven tools for spoken corpora analysis (Praat, Sonic Visualiser and a bridge to the R statistical package) in a modular system, facilitating automation and reuse. ![]() Researchers working with speech corpora are often faced with multiple tools and formats, and they need to work with ever-increasing amounts of data in a collaborative way. ![]() This paper presents Praaline, an open-source software system for managing, annotating, analysing and visualising speech corpora.
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