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We investigate the need for bigram alignment models and the benefit of supervised alignment techniques in graphemeto-phoneme (G2P) conversion. Moreover, we quantitatively estimate the relationship between alignment quality and overall G2P system performance. We find that, in English, bigram alignment models do perform better than unigram alignment models on the G2P task. Moreover, we find that supervised alignment techniques may perform considerably better than their unsupervised brethren and that few manually aligned training pairs suffice for them to do so. Finally, we estimate a highly significant impact of alignment quality on overall G2P transcription performance and that this relationship is linear in nature.

Do we need bigram alignment models? On the effect of alignment quality on transduction accuracy in G2P