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.
  • Allerhand, M., Butterfield, S., Cutler, A., & Patterson, R. (1992). Assessing syllable strength via an auditory model. In Proceedings of the Institute of Acoustics: Vol. 14 Part 6 (pp. 297-304). St. Albans, Herts: Institute of Acoustics.
  • Cutler, A., Kearns, R., Norris, D., & Scott, D. (1992). Listeners’ responses to extraneous signals coincident with English and French speech. In J. Pittam (Ed.), Proceedings of the 4th Australian International Conference on Speech Science and Technology (pp. 666-671). Canberra: Australian Speech Science and Technology Association.

    Abstract

    English and French listeners performed two tasks - click location and speeded click detection - with both English and French sentences, closely matched for syntactic and phonological structure. Clicks were located more accurately in open- than in closed-class words in both English and French; they were detected more rapidly in open- than in closed-class words in English, but not in French. The two listener groups produced the same pattern of responses, suggesting that higher-level linguistic processing was not involved in these tasks.
  • Cutler, A., & Robinson, T. (1992). Response time as a metric for comparison of speech recognition by humans and machines. In J. Ohala, T. Neary, & B. Derwing (Eds.), Proceedings of the Second International Conference on Spoken Language Processing: Vol. 1 (pp. 189-192). Alberta: University of Alberta.

    Abstract

    The performance of automatic speech recognition systems is usually assessed in terms of error rate. Human speech recognition produces few errors, but relative difficulty of processing can be assessed via response time techniques. We report the construction of a measure analogous to response time in a machine recognition system. This measure may be compared directly with human response times. We conducted a trial comparison of this type at the phoneme level, including both tense and lax vowels and a variety of consonant classes. The results suggested similarities between human and machine processing in the case of consonants, but differences in the case of vowels.
  • McQueen, J. M., & Cutler, A. (1992). Words within words: Lexical statistics and lexical access. In J. Ohala, T. Neary, & B. Derwing (Eds.), Proceedings of the Second International Conference on Spoken Language Processing: Vol. 1 (pp. 221-224). Alberta: University of Alberta.

    Abstract

    This paper presents lexical statistics on the pattern of occurrence of words embedded in other words. We report the results of an analysis of 25000 words, varying in length from two to six syllables, extracted from a phonetically-coded English dictionary (The Longman Dictionary of Contemporary English). Each syllable, and each string of syllables within each word was checked against the dictionary. Two analyses are presented: the first used a complete list of polysyllables, with look-up on the entire dictionary; the second used a sublist of content words, counting only embedded words which were themselves content words. The results have important implications for models of human speech recognition. The efficiency of these models depends, in different ways, on the number and location of words within words.
  • Norris, D., Van Ooijen, B., & Cutler, A. (1992). Speeded detection of vowels and steady-state consonants. In J. Ohala, T. Neary, & B. Derwing (Eds.), Proceedings of the Second International Conference on Spoken Language Processing; Vol. 2 (pp. 1055-1058). Alberta: University of Alberta.

    Abstract

    We report two experiments in which vowels and steady-state consonants served as targets in a speeded detection task. In the first experiment, two vowels were compared with one voiced and once unvoiced fricative. Response times (RTs) to the vowels were longer than to the fricatives. The error rate was higher for the consonants. Consonants in word-final position produced the shortest RTs, For the vowels, RT correlated negatively with target duration. In the second experiment, the same two vowel targets were compared with two nasals. This time there was no significant difference in RTs, but the error rate was still significantly higher for the consonants. Error rate and length correlated negatively for the vowels only. We conclude that RT differences between phonemes are independent of vocalic or consonantal status. Instead, we argue that the process of phoneme detection reflects more finely grained differences in acoustic/articulatory structure within the phonemic repertoire.
  • Cutler, A. (1983). Semantics, syntax and sentence accent. In M. Van den Broecke, & A. Cohen (Eds.), Proceedings of the Tenth International Congress of Phonetic Sciences (pp. 85-91). Dordrecht: Foris.

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