Ingo Siegert


2024

pdf bib
Proceedings of the Workshop on Legal and Ethical Issues in Human Language Technologies @ LREC-COLING 2024
Ingo Siegert | Khalid Choukri
Proceedings of the Workshop on Legal and Ethical Issues in Human Language Technologies @ LREC-COLING 2024

pdf bib
User Perspective on Anonymity in Voice Assistants – A comparison between Germany and Finland
Ingo Siegert | Silas Rech | Tom Bäckström | Matthias Haase
Proceedings of the Workshop on Legal and Ethical Issues in Human Language Technologies @ LREC-COLING 2024

2022

pdf bib
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference
Ingo Siegert | Mickael Rigault | Victoria Arranz
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

pdf bib
Pseudonymisation of Speech Data as an Alternative Approach to GDPR Compliance
Pawel Kamocki | Ingo Siegert
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

pdf bib
Public Interactions with Voice Assistant – Discussion of Different One-Shot Solutions to Preserve Speaker Privacy
Ingo Siegert | Yamini Sinha | Gino Winkelmann | Oliver Jokisch | Andreas Wendemuth
Proceedings of the Workshop on Ethical and Legal Issues in Human Language Technologies and Multilingual De-Identification of Sensitive Data In Language Resources within the 13th Language Resources and Evaluation Conference

2020

pdf bib
Alexa in the wild” – Collecting Unconstrained Conversations with a Modern Voice Assistant in a Public Environment
Ingo Siegert
Proceedings of the Twelfth Language Resources and Evaluation Conference

Datasets featuring modern voice assistants such as Alexa, Siri, Cortana and others allow an easy study of human-machine interactions. But data collections offering an unconstrained, unscripted public interaction are quite rare. Many studies so far have focused on private usage, short pre-defined task or specific domains. This contribution presents a dataset providing a large amount of unconstrained public interactions with a voice assistant. Up to now around 40 hours of device directed utterances were collected during a science exhibition touring through Germany. The data recording was part of an exhibit that engages visitors to interact with a commercial voice assistant system (Amazon’s ALEXA), but did not restrict them to a specific topic. A specifically developed quiz was starting point of the conversation, as the voice assistant was presented to the visitors as a possible joker for the quiz. But the visitors were not forced to solve the quiz with the help of the voice assistant and thus many visitors had an open conversation. The provided dataset – Voice Assistant Conversations in the wild (VACW) – includes the transcripts of both visitors requests and Alexa answers, identified topics and sessions as well as acoustic characteristics automatically extractable from the visitors’ audio files.

2019

pdf bib
Cross-Corpus Data Augmentation for Acoustic Addressee Detection
Oleg Akhtiamov | Ingo Siegert | Alexey Karpov | Wolfgang Minker
Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue

Acoustic addressee detection (AD) is a modern paralinguistic and dialogue challenge that especially arises in voice assistants. In the present study, we distinguish addressees in two settings (a conversation between several people and a spoken dialogue system, and a conversation between several adults and a child) and introduce the first competitive baseline (unweighted average recall equals 0.891) for the Voice Assistant Conversation Corpus that models the first setting. We jointly solve both classification problems, using three models: a linear support vector machine dealing with acoustic functionals and two neural networks utilising raw waveforms alongside with acoustic low-level descriptors. We investigate how different corpora influence each other, applying the mixup approach to data augmentation. We also study the influence of various acoustic context lengths on AD. Two-second speech fragments turn out to be sufficient for reliable AD. Mixup is shown to be beneficial for merging acoustic data (extracted features but not raw waveforms) from different domains that allows us to reach a higher classification performance on human-machine AD and also for training a multipurpose neural network that is capable of solving both human-machine and adult-child AD problems.

2012

pdf bib
Towards Emotion and Affect Detection in the Multimodal LAST MINUTE Corpus
Jörg Frommer | Bernd Michaelis | Dietmar Rösner | Andreas Wendemuth | Rafael Friesen | Matthias Haase | Manuela Kunze | Rico Andrich | Julia Lange | Axel Panning | Ingo Siegert
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

The LAST MINUTE corpus comprises multimodal recordings (e.g. video, audio, transcripts) from WOZ interactions in a mundane planning task (Rösner et al., 2011). It is one of the largest corpora with naturalistic data currently available. In this paper we report about first results from attempts to automatically and manually analyze the different modes with respect to emotions and affects exhibited by the subjects. We describe and discuss difficulties encountered due to the strong contrast between the naturalistic recordings and traditional databases with acted emotions.

2010

pdf bib
Developing an Expressive Speech Labeling Tool Incorporating the Temporal Characteristics of Emotion
Stefan Scherer | Ingo Siegert | Lutz Bigalke | Sascha Meudt
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

A lot of research effort has been spent on the development of emotion theories and modeling, however, their suitability and applicability to expressions in human computer interaction has not exhaustively been evaluated. Furthermore, investigations concerning the ability of the annotators to map certain expressions onto the developed emotion models is lacking proof. The proposed annotation tool, which incorporates the standard Geneva Emotional Wheel developed by Klaus Scherer and a novel temporal characteristic description feature, is aiming towards enabling the annotator to label expressions recorded in human computer interaction scenarios on an utterance level. Further, it is respecting key features of realistic and natural emotional expressions, such as their sequentiality, temporal characteristics, their mixed occurrences, and their expressivity or clarity of perception. Additionally, first steps towards evaluating the proposed tool, by analyzing utterance annotations taken from two expressive speech corpora, are undertaken and some future goals including the open source accessibility of the tool are given.