Ample evidence shows that word duration varies in a fine-grained way in response to a wide range of processing factors. Perhaps the most well-known and robust predictor of word duration in this literature is simply word frequency. Low-frequency words tend to be produced with longer duration than high-frequency words (Zipf, 1929), across a wide range of word types and categories (Bell et al., 2009). In fact, frequency controls significant variance in nearly every study mentioned in this section, and more of it than nearly any other factor (Jaeger, 2010). Conceptually, many of the other measures rely on frequency, as well, such as transitional
probably, which is the frequency of word w+1 following word w. The locus of the effect of word frequency in the production system is debated, with some claims that it has its primary effect on phonological form (Levelt et al., 1999), and competing evidence that lexical frequency also plays a role (Gahl, 2008). Regardless, frequency effects provide evidence that fluency of processing leads to chronometric variation, both on latency and duration.
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Most demonstrations of the effect of frequency on word duration take the form of corpus analyses that examine the probabilistic distribution of language, both spoken and written. Several different measures of probabilistic relationships between words show that words which precede high-frequency words generally have shorter durations than those which precede low-frequency words (Bell et al., 2009). The conditional probability of one word given some prior word, for example, is one such measure. This suggests that the production system either tracks these relationships in a vast statistical space, or has some other mechanism for modulating duration based on fluency of processing. Bell et al. propose a mechanism at the interface between semantic and phonological processing to explain the effect of surrounding words on word duration. When ongoing processing is delayed by extended word retrieval, as in the case of a low-frequency word, or having to activate a normally-unrelated word, the production system delays by extending the duration of the current word in order to avoid slipping into disfluency. Word lengthening, on this account, indexes the degree to which the system experiences
processing difficulty, and then smoothes word duration to accommodate fluctuations.
A similar proposal holds that prosodic variables such as phrase-final lengthening and accent placement explain most of the variance in word duration, even after accounting for frequency effects (Aylett & Turk, 2004). Unlike the Bell et al., (2009) study, Aylett and Turk represented prosody explicitly in their model alongside frequency effects. They found that prosodic variables did indeed explain a significant portion of the variance, but that frequency also contributed significantly. This suggests that the fluency of processing plays a role in word duration independently of its contribution to prosody (cf. Arnold & Watson, 2012).
Other frequency-as-fluency effects pervade the production system. The size of a word’s neighborhood affects its duration. Neighbors are typically defined as words that can be formed
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by changing, adding, or deleting phonemes from some target word. Cat’s neighbors, for
example, include words like bat, scat, and at. Words with many high-frequency neighbors also tend to have shorter durations than those with few, or low-frequency neighbors (Gahl, Yao & Johnson, 2012). Low-frequency words with high-frequency homophones also tend to have shorter pronunciation durations than those without (Bybee, 1999). Words with low phonotactic probability (the transitional probability between two or more phonemes) tend to have longer durations than those with high probability (Vitevich, Armbrüster & Chu, 2004). Syntactic
regularity (i.e. subcategorization frequency) affects the duration of words that appear at syntactic junctures (Gahl & Garnsey, 2004). All of these effects suggest that language production is sensitive to fluctuations in processing fluency, and in fact tracks several types of probabilistic relationships when determining exactly how long a duration to give a particular word.
Generally speaking, easy processing leads to shorter word durations, and more difficult
processing leads to longer word durations. This could happen for several reasons. Most directly, a speaker may elongate or shorten a word in order to promote or maintain speaking fluency. In a multi-word utterance, some words may be more difficult to process than others, which means that in order to speak fluently, speakers may have to manage the precise duration of each word. One possible explanation is that more difficult-to-process words may lead to increased duration on either themselves or earlier words, depending on how the system manages duration. Although an explanation like this has been offered to explain variation in spoken word duration (Bell et al., 2009), the exact nature of the relationship between ease of processing, multi-word utterances, and word duration remains an open empirical question.
Another possible explanation for the putative relationship between ease of processing and word duration lies in the production system’s assessment of the ease of processing itself. The
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system may know approximately how long semantic processing should take for a given word, a simple quantity or range of quantities to track. When selecting some semantic entity, the
production system could easily compare the expected difficulty of processing to the observed, and send this information along as a signal to latter sub-processes that it found a disparity. A lower observed value than expected might lead the system to say, “Hurry up, we’ve got this covered!” Conversely, a higher-than-expected value might cause the system to throw the brakes, slowing things down to prevent disfluency. Simple comparators like this could exist throughout the language production system (and cognition generally), micromanaging subsequent processes in order to achieve a desired level of fluency.
Both the explanation in terms of coordinating between words and the explanation in terms of processing difficulty on a particular word rely on the assumption that speakers desire fluency and work to maintain it. Shannon (1948) characterizes this process as the smoothing of the mathematically-defined amount of information transmission over a noisy channel. The information a word contributes to a conversation is measured in terms of its probability of appearing in a context. In a noisy information channel like speech, maintaining uniform information density over time maximizes communicative efficiency. Speakers do appear to modulate their utterances based on information density, even over and above the other
probabilistic measures mentioned so far (Jaeger, 2010). This includes syntactic variation (Jaeger, 2006), use of contractions (Levy & Jaeger, 2007), and even overall sentence complexity (Genzel & Charniak, 2002). The noisy channel is one of two things: either the processing from message to speech, or the interaction between speech and the listener(s). In both cases, it makes sense to characterize duration variation in terms of ensuring a balance between least effort and reliable
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transmission. Modulating word duration, in addition to engaging in some non-negligible amount of pre-planning, is a speaker’s primary means of remaining fluent.
All of the evidence reviewed here points to ease of processing having some influence on word duration. However, the majority of these studies make use of probabilistic methods, without any direct manipulation of variables. This allows for a more targeted look at which variables (e.g. neighborhood density or mutual information) have an influence, but is less conclusive than experimental methods, because a causal relationship is more difficult to establish. The experimental methods employed below take a step further toward establishing a causal relationship between sub-processes like lexical or phonological retrieval and variation in latency and duration. They ask similar questions about the relationships among the variables, but introduce more-direct manipulation. This represents what I consider the most productive union between statistical/corpus-based methods and experimental methods, with one supporting the other.