Anne Cutler †

Publications

Displaying 1 - 7 of 7
  • 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.
  • Cutler, A., Andics, A., & Fang, Z. (2011). Inter-dependent categorization of voices and segments. In W.-S. Lee, & E. Zee (Eds.), Proceedings of the 17th International Congress of Phonetic Sciences [ICPhS 2011] (pp. 552-555). Hong Kong: Department of Chinese, Translation and Linguistics, City University of Hong Kong.

    Abstract

    Listeners performed speeded two-alternative choice between two unfamiliar and relatively similar voices or between two phonetically close segments, in VC syllables. For each decision type (segment, voice), the non-target dimension (voice, segment) either was constant, or varied across four alternatives. Responses were always slower when a non-target dimension varied than when it did not, but the effect of phonetic variation on voice identity decision was stronger than that of voice variation on phonetic identity decision. Cues to voice and segment identity in speech are processed inter-dependently, but hard categorization decisions about voices draw on, and are hence sensitive to, segmental information.
  • Tuinman, A., Mitterer, H., & Cutler, A. (2011). The efficiency of cross-dialectal word recognition. In Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011), Florence, Italy (pp. 153-156).

    Abstract

    Dialects of the same language can differ in the casual speech processes they allow; e.g., British English allows the insertion of [r] at word boundaries in sequences such as saw ice, while American English does not. In two speeded word recognition experiments, American listeners heard such British English sequences; in contrast to non-native listeners, they accurately perceived intended vowel-initial words even with intrusive [r]. Thus despite input mismatches, cross-dialectal word recognition benefits from the full power of native-language processing.
  • Wagner, M., Tran, D., Togneri, R., Rose, P., Powers, D., Onslow, M., Loakes, D., Lewis, T., Kuratate, T., Kinoshita, Y., Kemp, N., Ishihara, S., Ingram, J., Hajek, J., Grayden, D., Göcke, R., Fletcher, J., Estival, D., Epps, J., Dale, R. and 11 moreWagner, M., Tran, D., Togneri, R., Rose, P., Powers, D., Onslow, M., Loakes, D., Lewis, T., Kuratate, T., Kinoshita, Y., Kemp, N., Ishihara, S., Ingram, J., Hajek, J., Grayden, D., Göcke, R., Fletcher, J., Estival, D., Epps, J., Dale, R., Cutler, A., Cox, F., Chetty, G., Cassidy, S., Butcher, A., Burnham, D., Bird, S., Best, C., Bennamoun, M., Arciuli, J., & Ambikairajah, E. (2011). The Big Australian Speech Corpus (The Big ASC). In M. Tabain, J. Fletcher, D. Grayden, J. Hajek, & A. Butcher (Eds.), Proceedings of the Thirteenth Australasian International Conference on Speech Science and Technology (pp. 166-170). Melbourne: ASSTA.
  • 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., & Butterfield, S. (1986). The perceptual integrity of initial consonant clusters. In R. Lawrence (Ed.), Speech and Hearing: Proceedings of the Institute of Acoustics (pp. 31-36). Edinburgh: Institute of Acoustics.

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