Matthias Sperber

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BACKGROUND The study of microbial diversity and community structures heavily relies on the analyses of sequence data, predominantly taxonomic marker genes like the small subunit of the ribosomal RNA (SSU rRNA) amplified from environmental samples. Until recently, the "gold standard" for this strategy was the cloning and Sanger sequencing of amplified target(More)
In this paper, we study the problem of manually correcting automatic annotations of natural language in as efficient a manner as possible. We introduce a method for automatically segmenting a corpus into chunks such that many uncertain labels are grouped into the same chunk, while human supervision can be omitted altogether for other segments. A tradeoff(More)
We propose a method for efficient off-line speech transcription through respeaking. Speech is segmented into smaller utterances using an initial automatic transcript. Respeaking is performed segment by segment, while confidence filtering helps save supervision effort. We conduct detailed experiments comparing speaking vs. typing, sequential vs.(More)
This paper describes our English Speech-to-Text (STT) systems for the 2012 IWSLT TED ASR track evaluation. The systems consist of 10 subsystems that are combinations of different front-ends, e.g. MVDR based and MFCC based ones, and two different phone sets. The outputs of the subsystems are combined via confusion network combination. Decoding is done in two(More)
In this paper, we present the KIT translation systems as well as the KIT-LIMSI systems for the ACL 2016 First Conference on Machine Translation. We participated in the shared task Machine Translation of News and submitted translation systems for three different directions: English→German, German→English and English→Romanian. We used a phrase-based machine(More)
This paper describes the KIT-NAIST (Contrastive) English speech recognition system for the IWSLT 2012 Evaluation Campaign. In particular, we participated in the ASR track of the IWSLT TED task. The system was developed by Karl-sruhe Institute of Technology (KIT) and Nara Institute of Science and Technology (NAIST) teams in collaboration within the interACT(More)
In this paper, we present the KIT systems of the IWSLT 2016 machine translation evaluation. We participated in the machine translation (MT) task as well as the spoken language language translation (SLT) track for English→German and German→English translation. We use attentional neural machine translation (NMT) for all our submissions. We investigated(More)
We present a speech transcription tool targeted at situations in which cost is a critical or limiting factor. This tool actively guides the transcription process by taking an automatically created transcript as a starting point, and asking for correction of only the parts likely to contain errors. The transcriber specifies a time budget, and the software(More)