STAR: articulation training for young children

Bunnell, H. Timothy; Yarrington, Debra; Polikoff, James B.

doi:10.21437/icslp.2000-757

Cited by 26 publications

(1 citation statement)

References 7 publications

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“…Several machine learning approcahes like Hidden Markov Models (HMM) [7], Linear Discriminant Analysis (LDA) [8] and Support Vector Machines [9] have been used to detect errors as well as to provide feedback. While reported performances of such methods in CAPT systems have been improving rapidly, the low accuracy of such feedback systems may discourage students from utilizing them [4].…”

Section: Introductionmentioning

confidence: 99%

Using an ensemble of classifiers for mispronunciation feedback

Ananthakrishnan,

Wik,

Engwall

et al. 2011

Speech and Language Technology in Education (SLaTE 2011)

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This paper proposes a paradigm where commonly made segmental pronunciation errors are modeled as pair-wise confusions between two or more phonemes in the language that is being learnt. The method uses an ensemble of support vector machine classifiers with time varying Mel frequency cepstral features to distinguish between several pairs of phonemes. These classifiers are then applied to classify the phonemes uttered by second language learners. Instead of providing feedback at every mispronounced phoneme, the method attempts to provide feedback about typical mispronunciations by a certain student, over an entire session of several utterances. Two case studies that demonstrate how the paradigm is applied to provide suitable feedback to two students is also described in this paper.

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Section: Introductionmentioning

confidence: 99%

Using an ensemble of classifiers for mispronunciation feedback

Ananthakrishnan,

Wik,

Engwall

et al. 2011

Speech and Language Technology in Education (SLaTE 2011)

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Design of a braille writing tutor to combat illiteracy

et al. 2009

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Less than 3% of the 145 million blind people living in developing countries are literate (Helander, Prejudice and dignity: An introduction to communitybased rehabilitation. New York: UNDP 1998). This low literacy rate is partly due to the lack of trained teachers and the challenges associated with learning to write braille on a traditional slate and stylus. These challenges include writing from right to left, writing mirrored images of letters, and receiving significantly delayed feedback. Extensive conversations with the Mathru Educational Trust for the Blind near Bangalore, India, revealed the need for a robust, low-power, low-cost braille writing tutor. We present an iterative and participatory process resulting in the creation and refinement of a prototype braille writing tutor system. This system uses a novel input device to capture a student's activity on a slate using a stylus and uses a range of techniques to teach braille writing skills to both beginner and advanced students. We report on lessons learned from the implementation of this project and from a 6-week pilot study at Mathru, and outline future directions for improvement.

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