Andreas Liesenfeld


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From text to talk: Harnessing conversational corpora for humane and diversity-aware language technology
Mark Dingemanse | Andreas Liesenfeld
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Informal social interaction is the primordial home of human language. Linguistically diverse conversational corpora are an important and largely untapped resource for computational linguistics and language technology. Through the efforts of a worldwide language documentation movement, such corpora are increasingly becoming available. We show how interactional data from 63 languages (26 families) harbours insights about turn-taking, timing, sequential structure and social action, with implications for language technology, natural language understanding, and the design of conversational interfaces. Harnessing linguistically diverse conversational corpora will provide the empirical foundations for flexible, localizable, humane language technologies of the future.


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Scikit-talk: A toolkit for processing real-world conversational speech data
Andreas Liesenfeld | Gabor Parti | Chu-Ren Huang
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

We present Scikit-talk, an open-source toolkit for processing collections of real-world conversational speech in Python. First of its kind, the toolkit equips those interested in studying or modeling conversations with an easy-to-use interface to build and explore large collections of transcriptions and annotations of talk-in-interaction. Designed for applications in speech processing and Conversational AI, Scikit-talk provides tools to custom-build datasets for tasks such as intent prototyping, dialog flow testing, and conversation design. Its preprocessor module comes with several pre-built interfaces for common transcription formats, which aim to make working across multiple data sources more accessible. The explorer module provides a collection of tools to explore and analyse this data type via string matching and unsupervised machine learning techniques. Scikit-talk serves as a platform to collect and connect different transcription formats and representations of talk, enabling the user to quickly build multilingual datasets of varying detail and granularity. Thus, the toolkit aims to make working with authentic conversational speech data in Python more accessible and to provide the user with comprehensive options to work with representations of talk in appropriate detail for any downstream task. For the latest updates and information on currently supported languages and language resources, please refer to:

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Animosity and suffering: Metaphors of BITTERNESS in English and Chinese
Gabor Parti | Andreas Liesenfeld | Chu-Ren Huang
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation


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Predicting gender and age categories in English conversations using lexical, non-lexical, and turn-taking features
Andreas Liesenfeld | Gábor Parti | Yuyin Hsu | Chu-Ren Huang
Proceedings of the 34th Pacific Asia Conference on Language, Information and Computation


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MYCanCor: A Video Corpus of spoken Malaysian Cantonese
Andreas Liesenfeld
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Project Notes on building a conversational parser on top of a text parser: Towards a causal language tagger for spoken Chinese
Andreas Liesenfeld
Proceedings of the 13th Joint ISO-ACL Workshop on Interoperable Semantic Annotation (ISA-13)