Abstract not found

In a regression study of conversational speech, we show that frequency, contextual predictability and repetition have separate contributions to word duration, despite their substantial correlations. Moreover, content- and function-word durations are affected differently by their frequency and predictability. Content words are shorter when more frequent, and shorter when repeated, while function words are not so affected. Function words have shorter pronunciations, after controlling for frequency and predictability. While both content and function words are strongly affected by predictability from the word following them, sensitivity to predictability from the preceding word is largely limited to very frequent function words. The results support the view that content and function words are accessed differently in production. We suggest a lexical-access-based model of our results, in which frequency or repetition lead to shorter or longer word durations by causing faster or slower lexical access, mediated by a general mechanism that coordinates the pace of higher-level planning and the execution of the articulatory plan.

Speech is considered an efficient communication channel. This implies that the organization of utterances is such that more speaking effort is directed towards important parts than towards redundant parts. Based on a model of incremental word recognition, the importance of a segment is defined as its contribution to worddisambiguation. This importance is measured as the segmental information content, in bits. On a labeled Dutch speech corpus it is then shown that crucial aspects of the information structure of utterances partition the segmental information content and explain 90 % of the variance. Two measures of acoustical reduction, duration and spectral center of gravity, are correlated with the segmental information content in such a way that more important phonemes are less reduced. It is concluded that the organization of reduction according to conventional information structure does indeed increase efficiency.

This paper provides evidence for Genzel and Charniak’s (2002) entropy rate principle, which predicts that the entropy of a sentence increases with its position in the text. We show that this principle holds for individual sentences (not just for averages), but we also find that the entropy rate effect is partly an artifact of sentence length, which also correlates with sentence position. Secondly, we evaluate a set of predictions that the entropy rate principle makes for human language processing; using a corpus of eye-tracking data, we show that entropy and processing effort are correlated, and that processing effort is constant throughout a text. 1

Speech prosody is manifest in the acoustic signal through the modulation of pitch, loudness, duration, and source characteristics (voice quality), which combine to encode the prosodic structure of an utterance. Prosodic structure defines the location of prominent words and syllables, and the grouping of words into phonological phrases. Prosodic structure, in turn, relates the phonological form of an utterance to its morphological, syntactic, semantic, and pragmatic context. The listener’s task in comprehending speech includes decoding prosodic structure to aid in identifying the morphological, syntactic, semantic, and pragmatic contexts that comprise the meaning of the utterance. The research reported in this dissertation focuses on acoustic and perceptual evidence for prosody in spoken language, and the relationship between prosodic structure and higher levels of linguistic organization. The study adopts a computational approach that employs natural language processing tools, machine learning algorithms, and speech and signal pro-cessing techniques to investigate prosody in speech corpus data. In this study, I show that prosodic features of an utterance can be reliably predicted from a set of features that en-

We present a constancy rate principle governing language generation. We show that this principle implies that local measures of entropy (ignoring context) should increase with the sentence number. We demonstrate that this is indeed the case by measuring entropy in three di#erent ways. We also show that this e#ect has both lexical (which words are used) and non-lexical (how the words are used) causes.

hyperarticulation

Changes in care of articulation (COA) affect both the spectral and durational characteristics of speech. This can have severe repercussions on both the success of speech recognition, and the quality of speech synthesis. Although auto-segmentation has proven useful for measuring the durational effects of COA, an automatic spectral measurement has proven more problematic [5]. In this paper, we will explore the use of the acoustic log likelihoods generated by HMM autosegmentation as a measure of these changes in comparison with two phonetically motivated modeling systems based on vocalic F1/F2 values. When duration variation is controlled, the HMM output does not correlate with the human perception of vowel goodness, whereas, the phonetically motivated models do. 1.