A serious mobile game with visual feedback for training sibilant consonants

The distortion of sibilant sounds is a common type of speech sound disorder (SSD) in Portuguese speaking children. Speech and language pathologists (SLP) frequently use the isolated sibilants exercise to assess and treat this type of speech errors. While technological solutions like serious games can help SLPs to motivate the children on doing the exercises repeatedly, there is a lack of such games for this specific exercise. Another important aspect is that given the usual small number of therapy sessions per week, children are not improving at their maximum rate, which is only achieved by more intensive therapy. We propose a serious game for mobile platforms that allows children to practice their isolated sibilants exercises at home to correct sibilant distortions. This will allow children to practice their exercises more frequently, which can lead to faster improvements. The game, which uses an automatic speech recognition (ASR) system to classify the child sibilant productions, is controlled by the child’s voice in real time and gives immediate visual feedback to the child about her sibilant productions. In order to keep the computation on the mobile platform as simple as possible, the game has a client-server architecture, in which the external server runs the ASR system. We trained it using raw Mel frequency cepstral coefficients, and we achieved very good results with an accuracy test score of above 91% using support vector machines.