@inproceedings{georgila-etal-2012-practical,
    title = "Practical Evaluation of Human and Synthesized Speech for Virtual Human Dialogue Systems",
    author = "Georgila, Kallirroi  and
      Black, Alan  and
      Sagae, Kenji  and
      Traum, David",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Do{\u{g}}an, Mehmet U{\u{g}}ur  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
    month = may,
    year = "2012",
    address = "Istanbul, Turkey",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/fix-sig-urls/L12-1318/",
    pages = "3519--3526",
    abstract = "The current practice in virtual human dialogue systems is to use professional human recordings or limited-domain speech synthesis. Both approaches lead to good performance but at a high cost. To determine the best trade-off between performance and cost, we perform a systematic evaluation of human and synthesized voices with regard to naturalness, conversational aspect, and likability. We vary the type (in-domain vs. out-of-domain), length, and content of utterances, and take into account the age and native language of raters as well as their familiarity with speech synthesis. We present detailed results from two studies, a pilot one and one run on Amazon's Mechanical Turk. Our results suggest that a professional human voice can supersede both an amateur human voice and synthesized voices. Also, a high-quality general-purpose voice or a good limited-domain voice can perform better than amateur human recordings. We do not find any significant differences between the performance of a high-quality general-purpose voice and a limited-domain voice, both trained with speech recorded by actors. As expected, the high-quality general-purpose voice is rated higher than the limited-domain voice for out-of-domain sentences and lower for in-domain sentences. There is also a trend for long or negative-content utterances to receive lower ratings."
}