The Development of Conversation Training Therapy: A Concept Paper

Academic Article


  • Objectives To introduce the conceptual, theoretical, and practical foundations of a novel approach to voice therapy, called conversation training therapy (CTT), which focuses exclusively on voice awareness and efficient voice production in patient-driven conversational narrative, without the use of a traditional therapeutic hierarchy. CTT is grounded in motor learning theory, focused on training target voice goals in spontaneous, conversational speech in the first session and throughout. CTT was developed by a consensus panel of expert clinical voice-specialized speech-language pathologists (SLPs) and patients with voice problems. Study Design This is a prospective, clinical consensus design. Methods A preliminary CTT approach to voice therapy was developed by the first and last authors (J.G-S. and A.I.G.) and incorporated six interchangeable tenets: clear speech, auditory/kinesthetic awareness, rapport building, negative practice, basic training gestures, and prosody. Five expert voice-specialized clinical SLPs (consensus group) were then presented CTT and a discussion ensued. Later, an informal interview by a neutral third party person occurred for further recommendations for CTT. Results The CTT approach was modified to reflect all the consensus groups’ recommendations, which included the need for more detail and rationale in the program, troubleshooting suggestions, and the concern for potential challenges for novice clinicians. Conclusions CTT is a new therapy approach based on motor learning theory, which exclusively uses patient-driven conversational narrative as the sole therapeutic stimuli. CTT is conceptually innovative because it represents an approach to voice therapy developed without the use of a traditional therapeutic hierarchy. It is also developed using input from patients with voice disorders and expert clinical providers.
  • Authors

    Published In

  • Journal of Voice  Journal
  • Digital Object Identifier (doi)

    Pubmed Id

  • 20177776
  • Author List

  • Gartner-Schmidt J; Gherson S; Hapner ER; Muckala J; Roth D; Schneider S; Gillespie AI
  • Start Page

  • 563
  • End Page

  • 573
  • Volume

  • 30
  • Issue

  • 5