Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.

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Determining the location of phonemes is important to a number of speech applications, including training of automatic speech recognition systems, building text-to-speech systems, and research on human speech processing. Agreement of humans on the location of phonemes is, on average, 93.78% within 20 msec on a variety of corpora, and 93.49% within 20 msec on the TIMIT corpus. We describe a baseline forced-alignment system and a proposed system with several modifications to this baseline. Modifications include the addition of energy-based features to the standard cepstral feature set, the use of probabilities of a state transition given an observation, and the computation of probabilities of distinctive phonetic features instead of phoneme-level probabilities. Performance of the baseline system on the test partition of the TIMIT corpus is 91.48% within 20 msec, and performance of the proposed system on this corpus is 93.36% within 20 msec. The results of the proposed system are a 22% relative reduction in error over the baseline system, and a 14% reduction in error over results from a non-HMM alignment system. This result of 93.36% agreement is the best known reported result on the TIMIT corpus.

Dysarthria is a motor speech impairment affecting millions of people. Dysarthric speech can he far less intelligible than that of non-dysarthric speakers, causing significant communication difficulties. The goal of this work is to understand the effect that certain modifications have on the intelligibility of dysarthric speech. These modifications are designed to identify aspects of the speech signal or signal processing that may he especially relevant to the effectiveness of a system that transforms dysarthric speech to improve its intelligibility. A result of this study is that dysarthric speech can, in the hest case, he modified only at the short-term spectral level to improve intelligibility from 68% to 87%. A baseline transformation system using standard technology, however, does not show improvement in intelligibility. Prosody also has a significant @ < 0.05) effect on intelligibility.