Computational evidence for syllable structure within the lexicon

Two of the main arguments against storing syllable structure within the mental lexicon are resyllabification and storage costs. All articulated outputs are syllabified and some phonemes move from one syllable to another during connected speech. This is not a universal

174 phenomenon, however, as languages with simple syllable typologies such as CV or CVC would not resyllabify. However, languages with complex syllable types (e.g., V, CCV, etc) could potentially resyllabify by maximising the onset of syllables with consonants from the preceding syllable’s coda. If we hypothesise that phonemes have a syllabified representation within the lexicon, then this post-lexical syllabification during output is wasteful. It is assumed that this puts an unnecessary burden on the system. In addition, there is also the assumption that storing syllabified representations leads to more costs in storage with no benefits as the structure can be derived from language-specific phonotactic rules. A common assumption within linguistics is that any element that can be derived through pre-specified rules or causes redundancy would not be stored, but computed online. However, it is possible that the costs from computing are offset by benefits in storage (and vice versa). We verified the validity of these assumptions by calculating the resyllabification rates of three languages as well as storage costs of these languages in three separate speech production models.

7.2.1.1. Is resyllabification a problem?

The calculation of resyllabification rates used speech corpora rather than written texts.

This was done in order to get a closer approximation to resyllabification in the real world.

Resyllabification rates for Italian and Hindi were at the lower end of the spectrum (less than 1%) but English was a little higher. But the interesting finding was that even English didn’t resyllabify more than 33% of syllables, on average, and even this was mostly isolated to a small group of word pairs. This indicated that resyllabification isn’t a pervasive phenomenon that occurred throughout the language but only under specific circumstances (some word and morpheme edges). This means that stored syllables cannot be deemed wasteful as the vast majority of words are never resyllabified. The environments that encourage resyllabification are syllables without onsets (e.g., ‘nap in there’ /næp.ɪn.ðɛə/[næ.pɪn.ðɛə]) or with onset consonants that can occupy a satellite position following syllables with coda phonemes that

175 can form onset clusters (e.g., ‘bike’s pin’ /baɪks.pɪn/[baɪk.spɪn]). English contains quite a few function words without onsets (e.g., if, it, a, an, on, in, etc.) which can stimulate resyllabification. Such environments are rare in Italian with its open syllables at the end of words. They are also rare in Hindi as frequently occurring function words usually have onsets. Therefore, when considering the speech production models for a language such as English, LRM has a computational cost of 100% as syllabification done on all segments post-lexically. LEWISS, on the other hand, has a cost of 33% as words have lexical syllabification with post-lexical syllabification at word and morpheme edges. The Dell model cannot handle post-lexical resyllabification as phonemes can only occupy their original syllable position.

The costs are even lower for LEWISS in Italian and Hindi.

This analysis indicated that resyllabification cannot be used as an a priori argument against storing syllabic information as the computational costs of modifying some phonemes at morpheme boundaries is not high. The other argument against syllable structure in the lexicon is storage costs.

7.2.1.2. How much are the actual storage costs?

We compared the information storage of three speech production models: the Dell, LRM and LEWISS models. When we look at a single arbitrary word such as ‘cat’ /kæt/ and compare the information costs, the Dell model (which stored the word as /k^h/on, /æ/nu, /t/cd) would intuitively appear to be more costly than the LRM model (which stored the word as

/k/1, /æ/2, /t/3). This is because the Dell model needs to store a /k/_on and a /k/_cd separately while the LRM model only stored the phoneme once. For example, the Dell model doesn’t differentiate between a phonological level and a phonetic level so the syllable initial aspiration has to be specified with the stored phoneme as well. LRM can create syllable initial aspiration after post-lexical syllabification.

176 To get a more realistic picture of the lexicon, we computed the information content costs for all the monosyllabic words in the CELEX dictionary. This gave a better estimation as different syllabic positions will have an effect on the information content in the Dell model. However, as the LRM model stores the phonemes according to serial position, it costs more information content to keep the phonemes in place. The Dell model, on the other hand, seems to specify too much in that phonemes are essentially duplicated for onset and coda position. While this may seem to increase storage costs, the fact that each phoneme contains information that keeps it in place means that the Dell model is the least costly in terms of storage as the phonemes require less structural information to keep them in place. For example, a complex onset would need more structural information in the LRM model to keep it in place but requires less information in the Dell model as its pre-specified syllable position keeps it in place. LEWISS was found to be in between these two extremes as phonemes are not specified for syllable position but are kept in place by a hierarchical syllable frame.

This analysis showed that the Dell model is more efficient than the LRM or LEWISS model for storing words in the mental lexicon. The computational efficiency of the Dell model has been demonstrated in the past for single words (Dell, 1986) and sentences (Dell, 1988). However, the Dell model cannot handle resyllabification efficiently as phonemes in this model cannot move between syllable positions since they are pre-specified for a particular syllable position. This also limits its capacity to explain movement errors found in ordinary speakers as well as the speech errors of patients with language disorders. These errors do not consistently move phonemes to the same syllable position (bringing into question the idea of phonemes pre-specified for syllable position). However, it is possible that the Dell model could account for resyllabification and movement errors with some modification to its architecture. But we have seen that the LEWISS model can accommodate such features while also accounting for other errors made by patients.

177 7.2.1.3. What about initial storage?

The previous analysis assumed efficient storage for the mental lexicon. However, the actual nature of storage is an issue for any system dealing with memory. There are a number of models dealing with this issue, the most well-known being the Forster’s (1976) model for lexical access. In this model, entries are stored in serial order with word searches being conducted in serial order until a match is found. New entries are added to the bottom of the list and move up the hierarchy with frequent use. Another hypothesis is Content-Addressable Storage (CAS) which uses hash tags to specify the location of every new entry. CAS needs to specify available storage space into categories so that the same type of data will be stored in similar locations. This saves on retrieval time and reduces redundancies. This analysis wasn’t an attempt to find a justification for CAS but given the assumption that CAS is the best method of storage for memory we tried to see whether syllable structure provides an economical method to allow acquisition without wasting unused space in the system. This analysis is independent of the previous section, which was based on information theory, in that while the former dealt with real world data (i.e., speech corpora), this study was a thought experiment using formal calculation. CAS was used as a reference against which we could compare the relative storage that needs to be in place to allow phonological acquisition.

Specifying syllable structure was found to help contract the necessary phase space to a more manageable degree than free combination. While this is a speculative assumption, it is a good way to illustrate how syllable structure can actually help organise and store phonological elements as opposed to being wasteful.

This final analysis on initial storage costs is not an attempt to establish the nature of the mental lexicon or memory systems. Rather, it assumes that given CAS is the most likely way of organising words in the lexicon, syllable structure may be a better organising principle as opposed to more open systems. It must be noted that empirical evidence is not conclusive on

178 the organising principles of the mental lexicon or the fact that memory has such severe constraints. Therefore, unlike the previous studies into resyllabification and information content, this final study should be treated more as a thought experiment rather than direct empirical research.

This section justified the advantages of storing syllable structure within the mental lexicon using computational analyses. The LEWISS model that stores lexical items with an organised syllable structure and computes post-lexical syllabification at word and morpheme boundaries was found to be an efficient model in terms of both storage and computation.

While the Dell model was the most efficient in terms of storage, it cannot handle post-lexical syllabification. LRM on the other hand was found to be the least efficient for both storage and computation. With this computational evidence in hand, we then proceeded to collect empirical evidence to see whether it supports a model with lexically stored syllable structure.