Anne Cutler †

Publications

Displaying 1 - 8 of 8
  • Cutler, A., & Bruggeman, L. (2013). Vocabulary structure and spoken-word recognition: Evidence from French reveals the source of embedding asymmetry. In Proceedings of INTERSPEECH: 14th Annual Conference of the International Speech Communication Association (pp. 2812-2816).

    Abstract

    Vocabularies contain hundreds of thousands of words built from only a handful of phonemes, so that inevitably longer words tend to contain shorter ones. In many languages (but not all) such embedded words occur more often word-initially than word-finally, and this asymmetry, if present, has farreaching consequences for spoken-word recognition. Prior research had ascribed the asymmetry to suffixing or to effects of stress (in particular, final syllables containing the vowel schwa). Analyses of the standard French vocabulary here reveal an effect of suffixing, as predicted by this account, and further analyses of an artificial variety of French reveal that extensive final schwa has an independent and additive effect in promoting the embedding asymmetry.
  • Warner, N. L., McQueen, J. M., Liu, P. Z., Hoffmann, M., & Cutler, A. (2012). Timing of perception for all English diphones [Abstract]. Program abstracts from the 164th Meeting of the Acoustical Society of America published in the Journal of the Acoustical Society of America, 132(3), 1967.

    Abstract

    Information in speech does not unfold discretely over time; perceptual cues are gradient and overlapped. However, this varies greatly across segments and environments: listeners cannot identify the affricate in /ptS/ until the frication, but information about the vowel in /li/ begins early. Unlike most prior studies, which have concentrated on subsets of language sounds, this study tests perception of every English segment in every phonetic environment, sampling perceptual identification at six points in time (13,470 stimuli/listener; 20 listeners). Results show that information about consonants after another segment is most localized for affricates (almost entirely in the release), and most gradual for voiced stops. In comparison to stressed vowels, unstressed vowels have less information spreading to
    neighboring segments and are less well identified. Indeed, many vowels,
    especially lax ones, are poorly identified even by the end of the following segment. This may partly reflect listeners’ familiarity with English vowels’ dialectal variability. Diphthongs and diphthongal tense vowels show the most sudden improvement in identification, similar to affricates among the consonants, suggesting that information about segments defined by acoustic change is highly localized. This large dataset provides insights into speech perception and data for probabilistic modeling of spoken word recognition.
  • Burnham, D., Ambikairajah, E., Arciuli, J., Bennamoun, M., Best, C. T., Bird, S., Butcher, A. R., Cassidy, S., Chetty, G., Cox, F. M., Cutler, A., Dale, R., Epps, J. R., Fletcher, J. M., Goecke, R., Grayden, D. B., Hajek, J. T., Ingram, J. C., Ishihara, S., Kemp, N. and 10 moreBurnham, D., Ambikairajah, E., Arciuli, J., Bennamoun, M., Best, C. T., Bird, S., Butcher, A. R., Cassidy, S., Chetty, G., Cox, F. M., Cutler, A., Dale, R., Epps, J. R., Fletcher, J. M., Goecke, R., Grayden, D. B., Hajek, J. T., Ingram, J. C., Ishihara, S., Kemp, N., Kinoshita, Y., Kuratate, T., Lewis, T. W., Loakes, D. E., Onslow, M., Powers, D. M., Rose, P., Togneri, R., Tran, D., & Wagner, M. (2009). A blueprint for a comprehensive Australian English auditory-visual speech corpus. In M. Haugh, K. Burridge, J. Mulder, & P. Peters (Eds.), Selected proceedings of the 2008 HCSNet Workshop on Designing the Australian National Corpus (pp. 96-107). Somerville, MA: Cascadilla Proceedings Project.

    Abstract

    Large auditory-visual (AV) speech corpora are the grist of modern research in speech science, but no such corpus exists for Australian English. This is unfortunate, for speech science is the brains behind speech technology and applications such as text-to-speech (TTS) synthesis, automatic speech recognition (ASR), speaker recognition and forensic identification, talking heads, and hearing prostheses. Advances in these research areas in Australia require a large corpus of Australian English. Here the authors describe a blueprint for building the Big Australian Speech Corpus (the Big ASC), a corpus of over 1,100 speakers from urban and rural Australia, including speakers of non-indigenous, indigenous, ethnocultural, and disordered forms of Australian English, each of whom would be sampled on three occasions in a range of speech tasks designed by the researchers who would be using the corpus.
  • Cutler, A., Davis, C., & Kim, J. (2009). Non-automaticity of use of orthographic knowledge in phoneme evaluation. In Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009) (pp. 380-383). Causal Productions Pty Ltd.

    Abstract

    Two phoneme goodness rating experiments addressed the role of orthographic knowledge in the evaluation of speech sounds. Ratings for the best tokens of /s/ were higher in words spelled with S (e.g., bless) than in words where /s/ was spelled with C (e.g., voice). This difference did not appear for analogous nonwords for which every lexical neighbour had either S or C spelling (pless, floice). Models of phonemic processing incorporating obligatory influence of lexical information in phonemic processing cannot explain this dissociation; the data are consistent with models in which phonemic decisions are not subject to necessary top-down lexical influence.
  • Cutler, A., Murty, L., & Otake, T. (2003). Rhythmic similarity effects in non-native listening? In Proceedings of the 15th International Congress of Phonetic Sciences (PCPhS 2003) (pp. 329-332). Adelaide: Causal Productions.

    Abstract

    Listeners rely on native-language rhythm in segmenting speech; in different languages, stress-, syllable- or mora-based rhythm is exploited. This language-specificity affects listening to non- native speech, if native procedures are applied even though inefficient for the non-native language. However, speakers of two languages with similar rhythmic interpretation should segment their own and the other language similarly. This was observed to date only for related languages (English-Dutch; French-Spanish). We now report experiments in which Japanese listeners heard Telugu, a Dravidian language unrelated to Japanese, and Telugu listeners heard Japanese. In both cases detection of target sequences in speech was harder when target boundaries mismatched mora boundaries, exactly the pattern that Japanese listeners earlier exhibited with Japanese and other languages. These results suggest that Telugu and Japanese listeners use similar procedures in segmenting speech, and support the idea that languages fall into rhythmic classes, with aspects of phonological structure affecting listeners' speech segmentation.
  • Shi, R., Werker, J., & Cutler, A. (2003). Function words in early speech perception. In Proceedings of the 15th International Congress of Phonetic Sciences (pp. 3009-3012).

    Abstract

    Three experiments examined whether infants recognise functors in phrases, and whether their representations of functors are phonetically well specified. Eight- and 13- month-old English infants heard monosyllabic lexical words preceded by real functors (e.g., the, his) versus nonsense functors (e.g., kuh); the latter were minimally modified segmentally (but not prosodically) from real functors. Lexical words were constant across conditions; thus recognition of functors would appear as longer listening time to sequences with real functors. Eightmonth- olds' listening times to sequences with real versus nonsense functors did not significantly differ, suggesting that they did not recognise real functors, or functor representations lacked phonetic specification. However, 13-month-olds listened significantly longer to sequences with real functors. Thus, somewhere between 8 and 13 months of age infants learn familiar functors and represent them with segmental detail. We propose that accumulated frequency of functors in input in general passes a critical threshold during this time.
  • Cutler, A., Van Ooijen, B., & Norris, D. (1999). Vowels, consonants, and lexical activation. In J. Ohala, Y. Hasegawa, M. Ohala, D. Granville, & A. Bailey (Eds.), Proceedings of the Fourteenth International Congress of Phonetic Sciences: Vol. 3 (pp. 2053-2056). Berkeley: University of California.

    Abstract

    Two lexical decision studies examined the effects of single-phoneme mismatches on lexical activation in spoken-word recognition. One study was carried out in English, and involved spoken primes and visually presented lexical decision targets. The other study was carried out in Dutch, and primes and targets were both presented auditorily. Facilitation was found only for spoken targets preceded immediately by spoken primes; no facilitation occurred when targets were presented visually, or when intervening input occurred between prime and target. The effects of vowel mismatches and consonant mismatches were equivalent.
  • Shattuck-Hufnagel, S., & Cutler, A. (1999). The prosody of speech error corrections revisited. In J. Ohala, Y. Hasegawa, M. Ohala, D. Granville, & A. Bailey (Eds.), Proceedings of the Fourteenth International Congress of Phonetic Sciences: Vol. 2 (pp. 1483-1486). Berkely: University of California.

    Abstract

    A corpus of digitized speech errors is used to compare the prosody of correction patterns for word-level vs. sound-level errors. Results for both peak F0 and perceived prosodic markedness confirm that speakers are more likely to mark corrections of word-level errors than corrections of sound-level errors, and that errors ambiguous between word-level and soundlevel (such as boat for moat) show correction patterns like those for sound level errors. This finding increases the plausibility of the claim that word-sound-ambiguous errors arise at the same level of processing as sound errors that do not form words.

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