Children´s speech recognition with application to interactive books and tutors

We present initial work towards development of a children´s speech recognition system for use within an interactive reading and comprehension training system. We first describe the Colorado Literacy Tutor project and two corpora collected for children´s speech recognition research. Next, baseline speech recognition experiments are performed to illustrate the degree of acoustic mismatch for children in grades K through 5. It is shown that an 11.2% relative reduction in word error rate can be achieved through vocal tract normalization applied to children´s speech. Finally, we describe our baseline system for automatic recognition of spontaneously spoken story summaries. It is shown that a word error rate of 42.6% is achieved on the presented children´s story summarization task after using unsupervised MAPLR (maximum a posteriori linear regression) adaptation and VTLN (vocal tract length normalization) to compensate for inter-speaker acoustic variability. Based on this result, we point to promising directions for further study.