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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.

Superficially, read and spontaneous speech—the two main kinds of training data for automatic speech recognition—appear as complementary, but are equal: pairs of texts and acoustic signals. Yet, spontaneous speech is typically harder for recognition. This is usually explained by different kinds of variation and noise, but there is a more fundamental deviation at play: for read speech, the audio signal is produced by recitation of the given text, whereas in spontaneous speech, the text is transcribed from a given signal. In this review, we embrace this difference by presenting a first introduction of causal reasoning into automatic speech recognition, and describing causality as a tool to study speaking styles and training data. After breaking down the data generation processes of read and spontaneous speech and analysing the domain from a causal perspective, we highlight how data generation by annotation must affect the interpretation of inference and performance. Our work discusses how various results from the causality literature regarding the impact of the direction of data generation mechanisms on learning and prediction apply to speech data. Finally, we argue how a causal perspective can support the understanding of models in speech processing regarding their behaviour, capabilities, and limitations. [ABSTRACT FROM AUTHOR]

Previous research has shown that differences in the marking of prosodic boundaries exist not only between varieties of the same language, but also between speaking styles of the same variety. Our aim is to gain insights into how these two factors interact in the case of German and Austrian German read and conversational speech. Analysing four acoustic cues, representing durational and fundamental frequency (f0) measures, we observed that pause duration was the strongest cue to prosodic boundaries and that f0 reset was the weakest, in both varieties and across speaking styles. In read speech however, we noticed differences between the two varieties in the weighting given to the four cues. We then employed the four investigated acoustic cues for the annotation of the less-resourced variety, by examining ways of exploiting resources from the more well-resourced variety. Testing three automatic approaches for boundary detection, we obtained an overall high performance: 82.4% and 76.6% F-score for read and conversational speech, respectively. Moreover, our results showed that training on more data from the well-resourced variety did not outperform the same system trained on less data from the target variety. • Prosodic boundaries in German and Austrian German, read and conversational speech. • Pause is the strongest cue, f0 reset is the weakest. • Cues weighting is more similar in conversational speech between the two varieties. • Automatic detection systems using the cues performed well on both speaking styles. • Training in variety-mismatched condition was not better than matched condition. [ABSTRACT FROM AUTHOR]

Pluricentric languages are languages that are spoken in at least two countries where they have an official function and thus develop national varieties with specific linguistic and pragmatic features. Presently 43 languages have been identified as belonging to this category, for instance, English, Spanish, German, Bengali, Hindi and Urdu. This article forms an introduction to the special issue "Pluricentric Languages in Speech Science and Technology" by giving an overview of current challenges with respect to the development of speech and language resources, annotation and analysis tools, as well as speech technology services for pluricentric languages. The article discusses potential solutions that come from cross-fertilization: on the one hand, how phonetic and linguistic knowledge may contribute to advancements in speech technology, and on the other, how speech technology may facilitate phonetic and linguistic studies on pluricentric languages. In our discussion, we include the research methods and findings of the eight research articles of this special issue and point towards promising paths for future research in the field. [ABSTRACT FROM AUTHOR]