Chomsky and Halle (1968) and many formal linguists rely on the notion of a universally available phonetic space defined in discrete time. This assumption plays a central role in phonological theory. Discreteness at the phonetic level guarantees the discreteness of all other levels of language. But decades of phonetics research demonstrate that there exists no universal inventory of phonetic objects. We discuss three kinds of evidence: first, phonologies differ incommensurably. Second, some phonetic characteristics of languages depend on intrinsically temporal patterns, and, third, some linguistic sound categories within a language are different from each other despite a high degree of overlap that precludes distinctness. Linguistics has mistakenly presumed that speech can always be spelled with letter-like tokens. A variety of implications of these conclusions for research in phonology are discussed.* The generative paradigm of language description (Chomsky 1964, 1965, Chomsky & Halle 1968) has dominated linguistic thinking in the United States for many years. Its specific claims about the phonetic basis of linguistic analysis still provide the cornerstone of most linguistic research. Many criticisms have been raised against the phonetic claims of the Sound pattern of English (Chomsky & Halle 1968), some from early on

A series of arguments is presented showing that words are not stored in memory in a way that resembles the abstract, phonological code used by alphabetical orthographies or by linguistic analysis. Words are stored in a very concrete, detailed auditory code that includes nonlinguistic information including speaker’s voice properties and other details. Thus, memory for language resembles an exemplar memory and abstract descriptions (using letter-like units and speaker-invariant features) are probably computed on the fly whenever needed. One consequence of this hypothesis is that the study of phonology should be the study of generalizations across the speech of a community and that such a description will employ units (segments, syllable types, prosodic patterns, etc.) that are not necessarily employed as units in speakers’ memory for language. That is, the psychological units of language are not useful for description of linguistic generalizations and linguistic generalizations across a community are not useful for storing the language for speaker use.

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We provide a conceptual framework for understanding similarities and differences among various schemes of compositional representation, emphasizing problems that arise in modelling aspects of human language. We propose six abstract dimensions that suggest a space of possible compositional schemes. Temporality turns out to play a key role in defining several of these dimensions. From studying how schemes fall into this space, it is apparent that there is no single crucial difference between AI and connectionist approaches to representation. Large regions of the space of compositional schemes remain unexplored, such as the entire class of active, dynamic models that do composition in time. These models offer the possibility of parsing real-time input into useful segments, and thus potentially into linguistic units like words and phrases. Introduction What is the relationship between the kinds of symbolic representations deployed in "classical" cognitive models and repr...

The known lengthening effects of phrase final position and of contrastive emphasis have been predicted by Klatt to combine super-additively. In a new experiment texts elicited at a wide range of speaking rates were measured and the separate and combined effects of these lengthening factors were found to combine approximately additively at all rates studied. The proportion of lengthening attributable to each factor was found to be relatively invariant except at the fastest speaking rates, where lengthening was eventually eliminated. The results support the interpretation of absolute speaking rate as an inessential variable for characterizing speech at a range of moderate rates. PACS numbers: 43.70.Fq, 43.70.Bk I. Introduction This paper deals with the individual and combined effects of three of the best known factors which influence macroscopic speech timing: speech rate, phrase final lengthening and contrastive emphasis. Phrase final lengthening (hereafter, PFL) refers to the relativel...

information is available. So too are the relative and absolute durations of segments and local and global rates of change, together with higher derivatives. Periodicities may yield information about rhythmic structure. As yet, no good taxonomy of the forms of temporal structure which encode information is available. We may look at a highly structured temporal signal such as speech to see some of the variety and complexity involved: ffl Overall speaking rate conveys salient information about a speaker's affect and the relative urgency of an utterance. Detecting the rate of a signal which has yet to be identified is a notoriously hard problem [14, 2] yet one which seems to be relatively easy for human perceptual systems. ffl Relative duration often carries linguistically significant information. Port and Dalby [19] identified the ratio of consonant to vowel duration as being a cue for determining whether a syllable final consonant is voiced (e.g

Psychoacoustic studies show that human listeners are sen- sitive to speaking rate variations [t0]. Automatic speech recognition (ASR) systems are even more affected by the changes in rate, as double to quadruple word recognition error rates of average speakers have been observed for fast speakers on many ASR systems [6]. In our earher work [5], we studied the causes of higher error and concluded that both the acoustic-phonetic and the phonological differences are sources of higher word error rates. In this work, we have studied various measures for quantifying rate of speech (ROS), and used simple methods for estimating the speak- ing rate of a novel utterance using ASR technology. We have also implemented mechanisms that make our ASR system more robust to fast speech. Using our ROS estimator to identify fast sentences in the test set, our rate-dependent system has 24.5% fewer errors on the fastest sentences and 6.2% fewer errors on all sentences of the WSJ93 evaluation set relative to the basehue HMM/MLP system.

The problem of speech patterns in time

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