We present a novel and general approach for fast and efficient non-sequential browsing of sound in large archives that we know little or nothing about, e.g. so called found data - data not recorded with the specific purpose to be analysed or used as training data. Our main motivation is to address some of the problems speech and speech technology researchers see when they try to capitalise on the huge quantities of speech data that reside in public archives. Our method is a combination of audio browsing through massively multi-object sound environments and a well-known unsupervised dimensionality reduction algorithm (SOM). We test the process chain on four data sets of different nature (speech, speech and music, farm animals, and farm animals mixed with farm sounds). The methods are shown to combine well, resulting in rapid and readily interpretable observations. Finally, our initial results are demonstrated in prototype software which is freely available.

The use of speech as an interaction modality has grown considerably through the integration of Intelligent Personal Assistants (IPAs- e.g. Siri, Google Assistant) into smartphones and voice based devices (e.g. Amazon Echo). However, there remain significant gaps in using theoretical frameworks to understand user behaviours and choices and how they may applied to specific speech interface interactions. This part-day multidisciplinary workshop aims to critically map out and evaluate theoretical frameworks and methodological approaches across a number of disciplines and establish directions for new paradigms in understanding speech interface user behaviour. In doing so, we will bring together participants from HCI and other speech related domains to establish a cohesive, diverse and collaborative community of researchers from academia and industry with interest in exploring theoretical and methodological issues in the field.

We demonstrate a method and a set of open source tools (beta) for nonsequential browsing of large amounts of audio data. The demonstration will contain versions of a set of functionalities in their first stages, and will provide a good insight in how the method can be used to browse through large quantities of audio data efficiently.

We introduce an expanded version of the Swedish research resource Språkbanken (the Swedish Language Bank). In 2018, Språkbanken, which has supported national and international research for over four decades, adds two branches, one focusing on speech and one on societal aspects of language, to its existing organization, which targets text. 

Child-directed spoken data is the ideal source of support for claims about children's linguistic environments. However, phonological transcriptions of child-directed speech are scarce, compared to sources like adult-directed speech or text data. Acquiring reliable descriptions of children's phonological environments from more readily accessible sources would mean considerable savings of time and money. The first step towards this goal is to quantify the reliability of descriptions derived from such secondary sources. We investigate how phonological distributions vary across different modalities (spoken vs. written), and across the age of the intended audience (children vs. adults). Using a previously unseen collection of Swedish adult-and child-directed spoken and written data, we combine lexicon look-up and graphemeto-phoneme conversion to approximate phonological characteristics. The analysis shows distributional differences across datasets both for single phonemes and for longer phoneme sequences. Some of these are predictably attributed to lexical and contextual characteristics of text vs. speech. The generated phonological transcriptions are remarkably reliable. The differences in phonological distributions between child-directed speech and secondary sources highlight a need for compensatory measures when relying on written data or on adult-directed spoken data, and/or for continued collection of actual child-directed speech in research on children's language environments.

In 2014, the Swedish government tasked a Swedish agency, The Swedish Post and Telecom Authority (PTS), with investigating how to best create and populate an infrastructure for spoken language resources (Ref N2014/2840/ITP). As a part of this work, the department of Speech, Music and Hearing at KTH Royal Institute of Technology have taken inventory of existing potential spoken language resources, mainly in Swedish national archives and other governmental or public institutions. In this position paper, key priorities, perspectives, and strategies that may be of general, rather than Swedish, interest are presented. We discuss broad types of potential spoken language resources available; to what extent these resources are free to use; and thirdly the main contribution: strategies to ensure the continuous acquisition of spoken language resources in a manner that facilitates speech and speech technology research.

The paper describes the design of a novel corpus of respiratory activity in spontaneous multiparty face-to-face conversations in Swedish. The corpus is collected with the primary goal of investigating the role of breathing for interactive control of interaction. Physiological correlates of breathing are captured by means of respiratory belts, which measure changes in cross sectional area of the rib cage and the abdomen. Additionally, auditory and visual cues of breathing are recorded in parallel to the actual conversations. The corpus allows studying respiratory mechanisms underlying organisation of spontaneous communication, especially in connection with turn management. As such, it is a valuable resource both for fundamental research and speech techonology applications.

This paper presents a first, largely qualitative analysis of a set of human-human dialogues recorded specifically to provide insights in how humans handle pauses and resumptions in situations where the speakers cannot see each other, but have to rely on the acoustic signal alone. The work presented is part of a larger effort to find unobtrusive human dialogue behaviours that can be mimicked and implemented in-car spoken dialogue systems within in the EU project Get Home Safe, a collaboration between KTH, DFKI, Nuance, IBM and Daimler aiming to find ways of driver interaction that minimizes safety issues,. The analysis reveals several human temporal, semantic/pragmatic, and structural behaviours that are good candidates for inclusion in spoken dialogue systems.

Children with speech disorders often present with systematic speech error patterns. In clinical assessments of speech disorders, evaluating the severity of the disorder is central. Current measures of severity have limited sensitivity to factors like the frequency of the target sounds in the child’s language and the degree of phonological diversity, which are factors that can be assumed to affect intelligibility. By constructing phonological filters to simulate eight speech error patterns often observed in children, and applying these filters to a phonologically transcribed corpus of 350K words, this study explores three quantitative measures of phonological impact: Percentage of Consonants Correct (PCC), edit distance, and degree of homonymy. These metrics were related to estimated ratings of severity collected from 34 practicing clinicians. The results show an expected high correlation between the PCC and edit distance metrics, but that none of the three metrics align with clinicians’ ratings. Although these results do not generate definite answers to what phonological factors contribute the most to (un)intelligibility, this study demonstrates a methodology that allows for large-scale investigations of the interplay between phonological errors and their impact on speech in context, within and across languages.