This paper presents a model which can automatically detect a variety of student speech acts as students collaborate within a computer supported collaborative learning environment. In addition, an analysis is presented which gives substantial insight as to how students’ learning is associated with students’ speech acts, knowledge that will significantly influence how this model is utilized by ru...

This paper proposes an efficient acoustic model adaptation method based on the use of simulated-data in maximum likelihood linear regression (MLLR) adaptation for robust speech recognition. Online MLLR adaptation is an unsupervised process which requires an input speech with phone labels transcribed automatically. Instead of using only the input signal in adaptation, our proposed simulated data...

We present the first unsupervised LSTM speech segmenter as a cognitive model of the acquisition of words from unsegmented input. Cognitive biases toward phonological and syntactic predictability in speech are rooted in the limitations of human memory (Baddeley et al., 1998); compressed representations are easier to acquire and retain in memory. To model the biases introduced by these memory lim...

Objectives: Learning disabilities can cause serious communication and socio-emotional disorders in students. Teachers, as specialists who are in direct contact with students on a daily basis, have an important role in identifying and referring students suspected of these disabilities. Therefore, this study investigated the role of primary school teachers’ awareness and attitude about the signs ...

Babbling is a crucial process in young infants for acquiring articulatory control. By constantly trying out motor commands and observing the consequences that they cause in the environment, they develop an understanding of their own body and learn to control their articulators in order to produce meaningful speech. In earlier works, we proposed a developmental model of speech acquisition that l...

This paper studies speech-driven Web retrieval models which accepts spoken search topics (queries) in the NTCIR-3 Web retrieval task. The major focus of this paper is on improving speech recognition accuracy of spoken queries and then improving retrieval accuracy in speech-driven Web retrieval. We experimentally evaluate the techniques of combining outputs of multiple LVCSR models in recognitio...