We present a multitier system for the remote administration of speech therapy to children with apraxia of speech. The system uses a client-server architecture model and facilitates task-oriented remote therapeutic training in both in-home and clinical settings. The system allows a speech language pathologist (SLP) to remotely assign speech production exercises to each child through a web interface and the child to practice these exercises in the form of a game on a mobile device. The mobile app records the child's utterances and streams them to a back-end server for automated scoring by a speech-analysis engine. The SLP can then review the individual recordings and the automated scores through a web interface, provide feedback to the child, and adapt the training program as needed. We have validated the system through a pilot study with children diagnosed with apraxia of speech, their parents, and SLPs. Here, we describe the overall client-server architecture, middleware tools used to build the system, speech-analysis tools for automatic scoring of utterances, and present results from a clinical study. Our results support the feasibility of the system as a complement to traditional face-to-face therapy through the use of mobile tools and automated speech analysis algorithms.

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We present a multi-tier system for the remote administration of speech therapy to children with apraxia of speech. The system uses a client-server architecture model and facilitates task-oriented remote therapeutic training in both in-home and clinical settings. Namely, the system allows a speech therapist to remotely assign speech production exercises to each child through a web interface, and the child to practice these exercises on a mobile device. The mobile app records the child's utterances and streams them to a back-end server for automated scoring by a speech-analysis engine. The therapist can then review the individual recordings and the automated scores through a web interface, provide feedback to the child, and adapt the training program as needed. We validated the system through a pilot study with children diagnosed with apraxia of speech, and their parents and speech therapists. Here we describe the overall client-server architecture, middleware tools used to build the system, the speech-analysis tools for automatic scoring of recorded utterances, and results from the pilot study. Our results support the feasibility of the system as a complement to traditional face-to-face therapy through the use of mobile tools and automated speech analysis algorithms.

Locating sign language (SL) videos on video sharing sites (e.g., YouTube) is challenging because search engines generally do not use the visual content of videos for indexing. Instead, indexing is done solely based on textual content (e.g., title, description, metadata). As a result, untagged SL videos do not appear in the search results. In this paper, we present and evaluate a classification approach to detect SL videos based on their visual content. The approach uses an ensemble of Haar-based face detectors to define regions of interest (ROI), and a background model to segment movements in the ROI. The two-dimensional (2D) distribution of foreground pixels in the ROI is then reduced to two 1D polar motion profiles by means of a polar-coordinate transformation, and then classified by means of an SVM. When evaluated on a dataset of user-contributed YouTube videos, the approach achieves 81% precision and 94% recall.

The Internet provides access to content in almost all languages through a combination of crawling, indexing, and ranking capabilities. The ability to locate content on almost any topic has become expected for most users. But it is not the case for those whose primary language is a sign language. Members of this community communicate via the Internet, but they pass around links to videos via email and social media. In this paper, we describe the need for, the architecture of, and initial software components of a distributed digital library of sign language content, called SLaDL. Our initial efforts have been to develop a model of collection development that enables community involvement without assuming it. This goal necessitated the development of video processing techniques that automatically detect sign language content in video.