Abstract: Recently, Automatic Speech Recognition (ASR) systems were used to assist children in language acquisition as it has the ability to detect human speech signal. Despite the benefits offered by the ASR system, there is a lack of ASR systems for Malay-speaking children. One of the contributing factors for this is the lack of continuous speech database for the target users. Though cross-lingual adaptation is a common solution for developing ASR systems for under-resourced language, it is not viable for children as there are very limited speech databases as a source model. In this research, we propose a two-stage adaptation for the development of ASR system for Malay-speaking children using a very limited database. The two stage adaptation comprises the cross-lingual adaptation (first stage) and cross-age adaptation. For the first stage, a well-known speech database that is phonetically rich and balanced, is adapted to the medium-sized Malay adults using supervised MLLR. The second stage adaptation uses the speech acoustic model generated from the first adaptation, and the target database is a small-sized database of the target users. We have measured the performance of the proposed technique using word error rate, and then compare them with the conventional benchmark adaptation. The two stage adaptation proposed in this research has better recognition accuracy as compared to the benchmark adaptation in recognizing children’s speech.
Abstract: Children today use computer based application in
various activities especially for learning and education. Many of
these tools and application such as the Computer Aided
Pronunciation Training (CAPT) systems enable children to explore
and experience them with little supervision from the adults. In order
for these tools and application to have maximum effect on the
children’s learning and education, it must be attractive to the children
to use them. This could be achieved with the proper user interface
(UI) design. As children grow, so do their ability, taste and
preferences. They interact differently with these applications as they
grow older. This study reviews several articles on how age factors
influence the UI design. The review focuses on age related abilities
such as cognitive, literacy, concentration and feedback requirement.
We have also evaluated few of existing CAPT systems and determine
the influence of age-based factors on the interface design.