Drawing on the findings of an ESRC-funded project, this article concerns the quality of teacher-pupil dialogue in the guided reading session of the Literacy Hour and its relationship to pupils' powers of comprehension. We recorded and analysed examples of discussions during the guided reading session in four primary schools, visiting each of them three times. On each school visit, we recorded the same group of six Year 6 children (10-11-year-olds) working with their class teacher. The results of the study suggest that talk in this context is teacher-dominated. The teacher: usually asks questions to which s/he already knows the answer; normally selects which pupil is going to speak next; keeps a tight grip on the topic of conversation; and does most of the talking. We argue that there is a case for making time available during the guided reading session for a period of discussion in which pupils are invited to play a more leading role in shaping the topic of conversation.

The purpose of this paper is to describe a new version of the Spoken English Corpus which will be of interest to phoneticians and other speech scientists. The Spoken English Corpus is a well-known collection of spoken-language texts that was collected and transcribed in the 1980's in a joint project involving IBM UK and the University of Lancaster (Alderson and Knowles forthcoming, Knowles and Taylor 1988). One valuable aspect of it is that the recorded material on which it was based is fairly freely available and the recording quality is generally good. At the time when the recordings were made, the idea of storing all the recorded material in digital form suitable for computer processing was of limited practicality. Although storage on digital tape was certainly feasible, this did not provide rapid computer access. The arrival of optical disk technology, with the possibility of storing very large amounts of digital data on a compact disk at relatively low cost, has brought about a revolution in ideas on database construction and use. It seemed to us that the recordings of the Spoken English Corpus (hereafter SEC) should now be converted into a form which would enable the user to gain access to the acoustic signal without the laborious business of winding through large amounts of tape. Once this was done, we should be able not only to listen to the recordings in a very convenient way, but also to carry out many automatic analyses of the material by computer.

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A study of emotional speech has resulted in a collection of some five hours of recorded material. The analysis of this material has required computer-based annotation incorporating prosodic and paralinguistic transcription as well as the coding of various psychological variables. A version of the prosodic and paralinguistic transcription devised by Crystal & Quirk was developed for use within the xwaves™ environment. This paper describes this transcription system and its application.

Recent procedures have been developed that allow the analysis of gestural stability across repetitions of phrase-length utterances by linearly normalizing aspects of the articulatory signal. This process produces an index of variability called spatiotemporal index, or STI (Smith, Goffman, Zelaznik, Ying, & McGillem, 1995). Consistent findings that different STIs underlie changes in speaking rates in normally speaking adults have been found in subsequent studies by Smith and colleagues. However some researchers have raised concerns that linearly normalized data do not adequately account for the nonlinear aspects in the articulatory signal (Lucero, Munhall, Gracco, & Ramsey, 1997). The present study compared findings from linear and nonlinear normalization procedures in the analysis of lower-lip displacement of phrase-length utterances for a group of 8 speakers and across three rates. Findings indicated that at a group level, gestural stability, although higher for each rate contingency, was similar to that found in earlier STI studies. However, variability was greater, and 4 of the 8 subjects failed to consistently demonstrate greater stability at habitual rate, followed by fast and then slow rate. A nonlinearly normalized analysis of the same data produced significantly lower stability indices, and variability was also reduced. It is argued that a nonlinear normalization procedure based on lower-lip displacement holds advantages in the analysis of phrase-length speech data over both linear and alternative nonlinear normalization techniques.