SBIR-STTR Award

Stuttering is a communication disorder characterized by disfluencies that are frequent and disruptive to communication. Clinicians extensively use disfluency counts to decide whether a client should be treated, to assess treatment progress, and to document treatment outcomes. Clinicians often do disfluency counts in real-time as a speaker is talking. However, these are not very specific, and cannot be re-examined. Clinicians can also use a verbatim transcription approach, in which they first transcribe exactly what was said, and then mark up the transcript with disfluency codes. This method allows more detailed and accurate counts to be obtained. The long term objective of this project is to build a computer tool that will assist clinicians in performing disfluency counts, both real-time and transcript based. The tool will allow both richer use of these counts, and, in the case of transcript-based counts, much less effort to create the counts. In fact, the amount of time should be reduced enough to enable transcript-based counts to be used in clinical practice. The goal of this Phase I-STTR is to demonstrate the feasibility of a computer tool to assist users in performing both real-time and transcript- based disfluency counts. For real-time counts, we will show that the tool achieves the same reliability and user acceptance as the pencil-and-paper method. We will also investigate whether the real-time counts can be re- examined and corrected (unlike the pencil-and-paper counts). For the transcript-based counts, we will show that the tool, for at least read-speech samples, allows the counts to be done substantially faster and with better reliability than current approaches. This will be achieved by incorporating an Automatic Speech Recognizer (ASR) that will use the story text to assist in transcribing the speech; and by incorporating a powerful user interface that allows the clinician to easily review and correct the automatic transcription. In Phase II, we will demonstrate the increased utility of disfluency counts due to them being stored in a computer file and time-aligned to the audio signal. We will extend the tool so that it can compare disfluency counts across multiple audio files. This will help clinicians better see the impact of their treatment over a period of time on the client's disfluency patterns. We will also demonstrate that the tool can assist clinicians with transcript- based counts of spontaneous speech. Again, we will incorporate an ASR to assist in the transcription process, and we will show that the tool allows transcript-based counts to be performed in substantially less time than current approaches. Furthermore, we will have a several clinicians use the tool over a period of several months with clients. This will demonstrate both the usefulness and practicality of the overall tool, and allow us to determine how to improve and augment it to best suit clinical needs.

Public Health Relevance:
Stuttering is a communication disorder characterized by disfluencies that are frequent and disruptive to communication. Clinicians extensively use disfluency counts to decide whether a client should be treated, to assess treatment progress, and to document treatment outcomes. This proposed project will create and evaluate a tool to assist clinicians in producing these counts, and enable more detailed and reliable counts to be employed in clinical practice.

Public Health Relevance:
Project Narrative Stuttering is a communication disorder characterized by disfluencies that are frequent and disruptive to com- munication. Clinicians extensively use disfluency counts to decide whether a client should be treated, to assess treatment progress, and to document treatment outcomes. This proposed project will create and evaluate a tool to assist clinicians in producing these counts, and enable more detailed and reliable counts to be employed in clinical practice.

Thesaurus Terms:
There Are No Thesaurus Terms On File For This Project.

Stuttering is a communication disorder characterized by disfluencies that may be frequent or disruptive to communication. Clinicians make extensive use of disfluency counts, for diagnosis, to assess treatment progress, and to document treatment outcomes. SLPs mostly do disfluency counts in real-time as a client is talking. However, this approach only accounts for the number of disfluencies, and not the different disfluency types, including the presence of disfluency clusters. Furthermore, the counts cannot be re-examined to improve accuracy. Alternatively, SLPs can use the verbatim transcript approach, in which they first transcribe exactly what was said, and then mark up the transcript with disfluency codes to provide the important information that is absent from real-time measures. Although verbatim transcripts provide a wealth of data that can support and improve clinical decision-making, this approach is rarely used, as it is very time-intensive. The goal of this project is to build computer tools that assist clinicians in performing detailed and more reliable disfluency counts and to determine how clinicians can better use these counts in clinical practice. The tool will allow both disfluency counts that are time-aligned to the audio file, and, for read-speech samples, counts that are part of an annotated verbatim transcript, which will provide the linguistic contexts in which the disfluencies occur. In fact, the amount of effort needed to create the transcript-based counts will be reduced enough to enable them to be routinely used in clinical practice. A key component is the use of Automatic Speech Recognition (ASR), which will allow (a) the number of fluent words to be automatically computed, allowing the clinician to focus on counting just the disfluencies;(b) the disfluencies in a read-speech sample to be easily revised for better reliability;and (c) the rapid creation of the annotated verbatim transcript for read-speech samples. To create an annotated verbatim transcript for a read-speech sample, the clinician will annotate the speaker's disfluencies with a computer tool. An ASR is then run, which does a rough transcription of what was said, taking into account where in the audio file the clinician annotated each disfluencies. Third, the clinician does a correction pass, in which they correct any errors in the disfluency annotations or in the word transcription. The aims of this proposal are as follows: (1) improve the annotation tool;(2) improve the ASR quality;(3) improve the user- interface of the correction tool;(4) determine how the information in the time-aligned annotations and annotated verbatim transcripts can be summarized so as to be useful in clinical practice;and (5) automate counting fluent words so that this does not need to be done by the SLPs.

Public Health Relevance:
Stuttering, which affects 1% of the U.S. population, is a communication disorder characterized by disfluencies that may be frequent or disruptive to communication. Clinicians extensively use disfluency counts to diagnose stuttering, to assess treatment progress, and to document treatment outcomes. This proposed project will create a computer tool to assist clinicians in performing detailed and more reliable disfluency counts and we will determine how clinicians can better use these counts in their clinical practice

Thesaurus Terms:
Accounting;Affect;Behavior;Client;Code;Coding System;Communication;Communication Disorders;Communication Impairment;Communicative Disorders;Computer Assisted;Computers;Data;Diagnosis;Evaluation;Focus Groups;Gene Transcription;Genetic Transcription;Goals;Grant;Interview;Language;Linguistic;Linguistics;Mammals, Mice;Measures;Mice;Modeling;Murine;Mus;Paper;Phase;Population;Process;Rna Expression;Reading;Recruitment Activity;Running;Sampling;Speech;Stuttering;Time;Transcript;Transcription;Transcription, Genetic;Translating;Translatings;Treatment Outcome;Base;Clinical Decision-Making;Clinical Practice;Computer Aided;Genetic Transcription;Improved;Language Translation;Model;Prototype;Recruit;Speech Recognition;Tool