Abstract-This study presents a preliminary investigation into the automatic assessment of language-impaired children's (LIC) prosodic skills in one grammatical aspect: sentence modalities. Three types of language impairments were studied: autism disorder (AD), pervasive developmental disorder-not otherwise specified (PDD-NOS), and specific language impairment (SLI). A control group of typically developing (TD) children that was both age and gender matched with LIC was used for the analysis. All of the children were asked to imitate sentences that provided different types of intonation (e.g., descending and rising contours). An automatic system was then used to assess LIC's prosodic skills by comparing the intonation recognition scores with those obtained by the control group. The results showed that all LIC have difficulties in reproducing intonation contours because they achieved significantly lower recognition scores than TD children on almost all studied intonations (p 0 05). Regarding the "Rising" intonation, only SLI children had high recognition scores similar to TD children, which suggests a more pronounced pragmatic impairment in AD and PDD-NOS children. The automatic approach used in this study to assess LIC's prosodic skills confirms the clinical descriptions of the subjects' communication impairments.

This paper analyzes the nature of the process involved in optional vowel reduction in Hungarian, and the acoustic structure of schwa variants in spontaneous speech. The study focuses on the acoustic patterns of both the basic realizations of Hungarian vowels and their realizations as neutral vowels (schwas), as well as on the design, implementation, and evaluation of a set of algorithms for the recognition of both types of realizations from the speech waveform. The authors address the question whether schwas form a unified group of vowels or they show some dependence on the originally intended articulation of the vowel they stand for. The acoustic study uses a database consisting of over 4,000 utterances extracted from continuous speech, and recorded from 19 speakers. The authors propose methods for the recognition of neutral vowels depending on the various vowels they replace in spontaneous speech. Mel-Frequency Cepstral Coefficients are calculated and used for the training of Hidden Markov Models. The recognition system was trained on 2,500 utterances and then tested on 1,500 utterances. The results show that a neutral vowel can be detected in 72% of all occurrences. Stressed and unstressed syllables can be distinguished in 92% of all cases. Neutralized vowels do not form a unified group of phoneme realizations. The pronunciation of schwa heavily depends on the original articulation configuration of the intended vowel.

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