We investigate grammatical error detection in spoken language, and present a data-driven method to train a dependency parser to automatically identify and label grammatical errors.This method is agnostic to the label set used, and the only manual annotations needed for training are grammatical error labels.We find that the proposed system is robust to disfluencies, so that a separate stage to elide disfluencies is not required.The proposed system outperforms two baseline systems on two different corpora that use different sets of error tags.It is able to identify utterances with grammatical errors with an F1-score as high as 0.623, as compared to a baseline F1 of 0.350 on the same data.

Deficits in semantic and pragmatic expression are among the hallmark linguistic features of autism. Recent work in deriving computational correlates of clinical spoken language measures has demonstrated the utility of automated linguistic analysis for characterizing the language of children with autism. Most of this research, however, has focused either on young children still acquiring language or on small populations covering a wide age range. In this paper, we extract numerous linguistic features from narratives produced by two groups of children with and without autism from two narrow age ranges. We find that although many differences between diagnostic groups remain constant with age, certain pragmatic measures, particularly the ability to remain on topic and avoid digressions, seem to improve. These results confirm findings reported in the psychology literature while underscoring the need for careful consideration of the age range of the population under investigation when performing clinically oriented computational analysis of spoken language.

We investigate the extent to which F0 can convey speaker ID in the absence of spectral, segmental, and durational information. We propose two methods of F0 synthesis based on the Linear Alignment Model (LAM) [2]: one parametric, the other corpusbased. Through a perceptual experiment, we show that F0 alone is able to convey information about speaker ID. We find that F0 synthesized with either LAM-based method conveys speaker ID almost as effectively as natural F0. Index Terms: F0, prosody, speech synthesis, speaker identity, recombinant synthesis

We present an analysis of F0range and peak alignment in emotional speech from a heterogeneous group of speakers varying in age and gender. Both speaker and emotion had a strong effect on F0range. Despite these large changes in the F0trajectory, peak alignment was remarkably stable. Using the Linear Alignment Model (LAM), we show that the effects on alignment of emotion and speaker differences, al though statistically significant, are small. This stability results in a conclusion that peak alignment, unlike F0range, does not appear to carry much information about speaker identity or emotional state. The LAM is effective in that it explains 42% of the variance in peak location on average, and furthermore it predicts the time of F0peaks with an average RMS error of 12ms.