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The robust and efficient extraction of features related to the glottal excitation source has become increasingly important for speech technology. The glottal open quotient (OQ) is one relevant measurement which is known to significantly vary with changes in voice quality on a breathy to tense continuum. The extraction of OQ, however, is hampered in the time-domain by the difficulty in consistently locating the point of glottal opening as well the computational load of its measurement. Determining OQ correlates in the frequency domain is an attractive alternative, however the lower frequencies of glottal source spectrum are also affected by other aspects of the glottal pulse shape thereby precluding closed-form solutions and straightforward mappings. The present study provides a comparison of three OQ estimation methods and shows a new method based on spectral features and artificial neural networks to outperform existing methods in terms of discrimination of voice quality, lower error values on a large volume of speech data and dramatically reduced computation time.