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Voice problems that arise during everyday vocal use can hardly be captured by standard outpatient voice assessments. In preparation for a digital health application to automatically assess longitudinal voice data 'in the wild' – the VocDoc , the aim of this paper was to study vocal fatigue from the speaker's perspective, the healthcare professional's perspective, and the 'machine's' perspective. We collected data of four voice healthy speakers completing a 90-min reading task. Every 10 min the speakers were asked about subjective voice characteristics. Then, we elaborated on the task of elapsed speaking time recognition: We carried out listening experiments with speech and language therapists and employed random forests on the basis of extracted acoustic features. We validated our models speaker-dependently and speaker-independently and analysed underlying feature importances. For an additional, clinical application-oriented scenario, we extended our dataset for lecture recordings of another two speakers. Self- and expert-assessments were not consistent. With mean F1 scores up to 0.78, automatic elapsed speaking time recognition worked reliably in the speaker-dependent scenario only. A small set of acoustic features – other than features previously reported to reflect vocal fatigue – was found to universally describe long-term variations of the voice. Vocal fatigue seems to have individual effects across different speakers. Machine learning has the potential to automatically detect and characterise vocal changes over time. Our study provides technical underpinnings for a future mobile solution to objectively capture pathological long-term voice variations in everyday life settings and make them clinically accessible. • A few acoustic features seem to universally describe vocal fatigue. • Vocal fatigue has rather individual effects across different speakers. • Machine learning has the potential to automatically detect effects of vocal fatigue. • A mobile app can capture clinically relevant long-term voice variations in the wild. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the realization of word-final /t/ in conversational standard Dutch. First, based on a large number of word tokens (6747) annotated with broad phonetic transcription by an automatic transcription tool, we show that morphological properties of the words and their position in the utterance''s syntactic structure play a role for the presence versus absence of their final /t/. We also replicate earlier findings on the role of predictability (word frequency and bigram frequency with the following word) and provide a detailed analysis of the role of segmental context. Second, we analyze the detailed acoustic properties of word-final /t/ on the basis of a smaller number of tokens (486) which were annotated manually. Our data show that word and bigram frequency as well as segmental context also predict the presence of sub-phonemic properties. The investigations presented in this paper extend research on the realization of /t/ in spontaneous speech and have potential consequences for psycholinguistic models of speech production and perception as well as for automatic speech recognition systems. [Copyright &y& Elsevier]

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]