Exploring complex vowels as phrase break correlates in a corpus of English speech with ProPOSEL, a prosody and POS English lexicon

Real-world knowledge of syntax is seen as integral to the machine learning task of phrase break prediction but there is a deficiency of a priori knowledge of prosody in both rule-based and data-driven classifiers. Speech recognition has established that pauses affect vowel duration in preceding words. Based on the observation that complex vowels occur at rhythmic junctures in poetry, we run significance tests on a sample of transcribed, contemporary British English speech and find a statistically significant correlation between complex vowels and phrase breaks. The experiment depends on automatic text annotation via ProPOSEL, a prosody and part-of-speech English lexicon. Copyright © 2009 ISCA

The Validation of Speech Corpora

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Improving TTS with corpus-specific pronunciation adaptation

International audienceText-to-speech (TTS) systems are built on speech corpora which are labeled with carefully checked and segmented phonemes. However, phoneme sequences generated by automatic grapheme-to-phoneme converters during synthesis are usually inconsistent with those from the corpus, thus leading to poor quality synthetic speech signals. To solve this problem , the present work aims at adapting automatically generated pronunciations to the corpus. The main idea is to train corpus-specific phoneme-to-phoneme conditional random fields with a large set of linguistic, phonological, articulatory and acoustic-prosodic features. Features are first selected in cross-validation condition, then combined to produce the final best feature set. Pronunciation models are evaluated in terms of phoneme error rate and through perceptual tests. Experiments carried out on a French speech corpus show an improvement in the quality of speech synthesis when pronunciation models are included in the phonetization process. Appart from improving TTS quality, the presented pronunciation adaptation method also brings interesting perspectives in terms of expressive speech synthesis

Can humain association norm evaluate latent semantic analysis?

This paper presents the comparison of word association norm created by a psycholinguistic experiment to association lists generated by algorithms operating on text corpora. We compare lists generated by Church and Hanks algorithm and lists generated by LSA algorithm. An argument is presented on how those automatically generated lists reflect real semantic relations