BACKGROUND English-speaking children with specific language impairment (SLI) perform more poorly than their typically developing peers in verbal working memory tasks where processing and storage are simultaneously required. Hungarian is a language with a relatively free word order and a rich agglutinative morphology. AIMS To examine the effect of linguistic structure on working memory perform...

Spoken Translation, Inc. (STI) of Berkeley, CA has developed a commercial system for interactive speechto-speech machine translation designed for both high accuracy and broad linguistic and topical coverage. Planned use is in situations requiring both of these features, for example in helping Spanish-speaking patients to communicate with English-speaking doctors, nurses, and other health-care s...

Since we cannot exclude that speech recognizers fail sometimes, it is important to examine how users react to recognition errors. In correction situations, speaking style becomes more accentuated to disambiguate the original mistake. We examine the effect of speaking style in such situations on speech recognition performance. Our results indicate that hyperarticulated effects occur in correctio...

Previous work on formant targets has assumed that these targets are independent of the speaking style. In this paper, we estimate consonant and vowel targets in a database of “clear” and “conversational” speech, using both style-independent and style-dependent models. The test-set errors and clustering of the estimated target values indicate that for this corpus, formant targets depend on the s...

The present study was conducted to evaluate the accuracy affecting factors of a Mel-Frequency Cepstral Coefficients (MFCC) and Vector Quantization (VQ) based speaker recognition system. This investigation analyses the factors that affecting recognition accuracy using speech signal from day to day life in surrounding environments. It was studied the mismatch affects of text-dependency, voice sam...

We have developed an automated method that predicts the word accuracy of a speech recognition system for non-native speech, in the context of speaking proficiency scoring. A model was trained using features based on speech recognizer scores, function word distributions, prosody, background noise, and speaking fluency. Since the method was implemented for non-native speech, fluency features, whi...