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This paper investigates the nature of reduction phenomena in informal speech. It addresses the question whether reduction processes that affect many word types, but only if they occur in connected informal speech, may be categorical in nature. The focus is on reduction of schwa in the prefixes and on word-final /t/ in Dutch past participles. More than 2000 tokens of past participles from the Ernestus Corpus of Spontaneous Dutch and the Spoken Dutch Corpus (both from the interview and read speech component) were transcribed automatically. The results demonstrate that the presence and duration of /t/ are affected by approximately the same phonetic variables, indicating that the absence of /t/ is the extreme result of shortening, and thus results from a gradient reduction process. Also for schwa, the data show that mainly phonetic variables influence its reduction but its presence is affected by different and more variables than its duration, which suggests that the absence of schwa may result from gradient as well as categorical processes. These conclusions are supported by the distributions of the segments' durations. These findings provide evidence that reduction phenomena which affect many words in informal conversations may also result from categorical reduction processes.

Acoustic metrics of contrastive speech rhythm, based on vocalic and intervocalic interval durations, are intended to capture stable typological differences between languages. They should consequently be robust to variation between speakers, sentence materials, and measurers. This paper assesses the impact of these sources of variation on the metrics %V (proportion of utterance comprised of vocalic intervals), VarcoV (rate-normalized standard deviation of vocalic interval duration), and nPVI-V (a measure of the durational variability between successive pairs of vocalic intervals). Five measurers analyzed the same corpus of speech: five sentences read by six speakers of Standard Southern British English. Differences between sentences were responsible for the greatest variation in rhythm scores. Inter-speaker differences were also a source of significant variability. However, there was relatively little variation due to segmentation differences between measurers following an agreed protocol. An automated phone alignment process was also used: Rhythm scores thus derived showed good agreement with the human measurers. A number of recommendations for researchers wishing to exploit contrastive rhythm metrics are offered in conclusion.

With the growing interest among speech scientists in working with natural conversations also the popularity for using articulatory-acoustic features as basic unit increased. They showed to be more suitable than purely phone-based approaches. Even though the motivation for AF classification is driven by the properties of conversational speech, most of the new methods continue to be developed on read speech corpora (e.g., TIMIT). In this paper, we show in two studies that the improvements obtained on read speech do not always transfer to conversational speech. The first study compares four different variants of acoustic parameters for AF classification of both read and conversational speech using support vector machines. Our experiments show that the proposed set of acoustic parameters substantially improves AF classification for read speech, but only marginally for conversational speech. The second study investigates whether labeling inaccuracies can be compensated for by a data selection approach. Again, although an substantial improvement was found with the data selection approach for read speech, this was not the case for conversational speech. Overall, these results suggest that we cannot continue to develop methods for one speech style and expect that improvements transfer to other styles. Instead, the nature of the application data (here: read vs. conversational) should be taken into account already when defining the basic assumptions of a method (here: segmentation in phones), and not only when applying the method to the application data [ABSTRACT FROM AUTHOR]

Abstract: In spontaneous, conversational speech, words are often reduced compared to their citation forms, such that a word like yesterday may sound like . The present chapter investigates such acoustic reduction. The study of reduction needs large corpora that are transcribed phonetically. The first part of this chapter describes an automatic transcription procedure used to obtain such a large phonetically transcribed corpus of Dutch spontaneous dialogues, which is subsequently used for the investigation of acoustic reduction. First, the orthographic transcriptions were adapted for automatic processing. Next, the phonetic transcription of the corpus was created by means of a forced alignment using a lexicon with multiple pronunciation variants per word. These variants were generated by applying phonological and reduction rules to the canonical phonetic transcriptions of the words. The second part of this chapter reports the results of a quantitative analysis of reduction in the corpus on the basis of the generated transcriptions and gives an inventory of segmental reductions in standard Dutch. Overall, we found that reduction is more pervasive in spontaneous Dutch than previously documented. [Copyright &y& Elsevier]