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In contrast to previous studies that look only at discriminating pathological voice from the normal voice, in this study we focus on the discrimination between cases of spasmodic dysphonia (SD) and vocal fold palsy (VP) using automated analysis of speech recordings. The hypothesis is that discrimination will be enhanced by studying continuous speech, since the different pathologies are likely to have different effects in different phonetic contexts. We collected audio recordings of isolated vowels and of a read passage from 60 patients diagnosed with SD (N=38) or VP (N=22). Baseline classifiers on features extracted from the recordings taken as a whole gave a cross-validated unweighted average recall of up to 75% for discriminating the two pathologies. We used an automated method to divide the read passage into phone-labelled regions and built classifiers for each phone. Results show that the discriminability of the pathologies varied with phonetic context as predicted. Since different phone contexts provide different information about the pathologies, classification is improved by fusing phone predictions, to achieve a classification accuracy of 83%. The work has implications for the differential diagnosis of voice pathologies and contributes to a better understanding of their impact on speech.