Parkinson's Disease (PD) is a neurodegenerative condition that affects the motor capabilities of individuals. Early detection can potentially contribute to slow its progression in a near future. Therefore, new objective and reliable tools are needed to support its diagnosis. Literature suggests that the patients' speech can provide relevant information about the presence of the disease. In this study, five sets of experiments were carried out, each containing new approaches to detect the presence of the disease in the speech of idiopathic PD patients and control speakers from three different corpora, two of them in the Spanish language. Different speech frame selection techniques are proposed, such as phonemic and acoustic landmark distillation, providing certain specific speech segments of interest to this work's purposes. Multiple cepstral and spectral features were employed, along with several classification techniques based on Gaussian models and speaker embeddings. The best accuracy results in detecting PD with the proposed methodologies reached values ranging from 85% to 94% with Area Under the Curve between 0.91 and 0.99, depending on the corpus. Results suggest that PD affects the movements related to all of the studied articulatory segmental groups but has a more evident influence in the consonants with a greater narrowing of the vocal tract, mainly plosives, and fricatives. The new proposed methodologies demonstrate their ability to support PD's diagnosis during a patient's clinical assessment and are a step forward in PD's speech-based diagnosis systems.