Anne Cutler †

Publications

Displaying 1 - 10 of 10
  • 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.
  • Cooper, N., & Cutler, A. (2004). Perception of non-native phonemes in noise. In S. Kin, & M. J. Bae (Eds.), Proceedings of the 8th International Conference on Spoken Language Processing (Interspeech 2004-ICSLP) (pp. 469-472). Seoul: Sunjijn Printing Co.

    Abstract

    We report an investigation of the perception of American English phonemes by Dutch listeners proficient in English. Listeners identified either the consonant or the vowel in most possible English CV and VC syllables. The syllables were embedded in multispeaker babble at three signal-to-noise ratios (16 dB, 8 dB, and 0 dB). Effects of signal-to-noise ratio on vowel and consonant identification are discussed as a function of syllable position and of relationship to the native phoneme inventory. Comparison of the results with previously reported data from native listeners reveals that noise affected the responding of native and non-native listeners similarly.
  • Cutler, A., Norris, D., & Sebastián-Gallés, N. (2004). Phonemic repertoire and similarity within the vocabulary. In S. Kin, & M. J. Bae (Eds.), Proceedings of the 8th International Conference on Spoken Language Processing (Interspeech 2004-ICSLP) (pp. 65-68). Seoul: Sunjijn Printing Co.

    Abstract

    Language-specific differences in the size and distribution of the phonemic repertoire can have implications for the task facing listeners in recognising spoken words. A language with more phonemes will allow shorter words and reduced embedding of short words within longer ones, decreasing the potential for spurious lexical competitors to be activated by speech signals. We demonstrate that this is the case via comparative analyses of the vocabularies of English and Spanish. A language which uses suprasegmental as well as segmental contrasts, however, can substantially reduce the extent of spurious embedding.
  • 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.
  • Van Ooijen, B., Cutler, A., & Berinetto, P. M. (1993). Click detection in Italian and English. In Eurospeech 93: Vol. 1 (pp. 681-684). Berlin: ESCA.

    Abstract

    We report four experiments in which English and Italian monolinguals detected clicks in continous speech in their native language. Two of the experiments used an off-line location task, and two used an on-line reaction time task. Despite there being large differences between English and Italian with respect to rhythmic characteristics, very similar response patterns were found for the two language groups. It is concluded that the process of click detection operates independently from language-specific differences in perceptual processing at the sublexical level.
  • Young, D., Altmann, G. T., Cutler, A., & Norris, D. (1993). Metrical structure and the perception of time-compressed speech. In Eurospeech 93: Vol. 2 (pp. 771-774).

    Abstract

    In the absence of explicitly marked cues to word boundaries, listeners tend to segment spoken English at the onset of strong syllables. This may suggest that under difficult listening conditions, speech should be easier to recognize where strong syllables are word-initial. We report two experiments in which listeners were presented with sentences which had been time-compressed to make listening difficult. The first study contrasted sentences in which all content words began with strong syllables with sentences in which all content words began with weak syllables. The intelligibility of the two groups of sentences did not differ significantly. Apparent rhythmic effects in the results prompted a second experiment; however, no significant effects of systematic rhythmic manipulation were observed. In both experiments, the strongest predictor of intelligibility was the rated plausibility of the sentences. We conclude that listeners' recognition responses to time-compressed speech may be strongly subject to experiential bias; effects of rhythmic structure are most likely to show up also as bias effects.
  • Cutler, A. (1987). Components of prosodic effects in speech recognition. In Proceedings of the Eleventh International Congress of Phonetic Sciences: Vol. 1 (pp. 84-87). Tallinn: Academy of Sciences of the Estonian SSR, Institute of Language and Literature.

    Abstract

    Previous research has shown that listeners use the prosodic structure of utterances in a predictive fashion in sentence comprehension, to direct attention to accented words. Acoustically identical words spliced into sentence contexts arc responded to differently if the prosodic structure of the context is \ aricd: when the preceding prosody indicates that the word will he accented, responses are faster than when the preceding prosodv is inconsistent with accent occurring on that word. In the present series of experiments speech hybridisation techniques were first used to interchange the timing patterns within pairs of prosodic variants of utterances, independently of the pitch and intensity contours. The time-adjusted utterances could then serve as a basis lor the orthogonal manipulation of the three prosodic dimensions of pilch, intensity and rhythm. The overall pattern of results showed that when listeners use prosody to predict accent location, they do not simply rely on a single prosodic dimension, hut exploit the interaction between pitch, intensity and rhythm.
  • Cutler, A., & Carter, D. (1987). The prosodic structure of initial syllables in English. In J. Laver, & M. Jack (Eds.), Proceedings of the European Conference on Speech Technology: Vol. 1 (pp. 207-210). Edinburgh: IEE.

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