A Random, Semantically Appropriate Sentence Generator for Speaker Verification

We describe two systems for automatically generating English sentences, and evaluate the suitability of their output for speaker verification. The first system, SUSGen, generates grammatical but semantically anomalous sentences of controlled length, vocabulary and phonetic content. The second system, SASGen, extends SUSGen to generate a greater variety of sentences and ones that are, for the most part, semantically acceptable. We demonstrate that sentences generated by SASGen are significantly more readable and meaningful than those generated by SUSGen. Sentences generated by SASGen are not as readable or meaningful as humangenerated sentences, but the additional control SASGen provides for sentence length, vocabulary and phonetic content make it more suitable for speaker verification and other voice collection purposes than harvesting from human-generated sentences.