TU Berlin

Quality and Usability LabMARS

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Recognition of Mobile and Rich Speech (MARS)

Motivation & Project Description 

  •  New model training algorithms for distant speech

    • Training using noise reduction algorithms and normalizing transforms
    • Context clustering for room acoustics
    • Non-native, multi-lingual and cross-lingual speech processing

  • Meta data extraction on distant, telephone, and wideband speech

    • ID, age, gender, emotion, channel, language, socio-economic status
    • Online acoustic change detection
    • Speaker detection, clustering and adaptation

  • In-house ASR system as benchmark for external suppliers

Expected Outcome:

  • Janus-based ASR modules using 16kHz English distant-speech AMs
  • Janus-based Inspire recognizer
  • Janus-based ASR for Ivistar info displays (with VCE)
  • Meta-data extraction modules and integration with Janus Recognition Toolkit
Time Frame: 

T-labs Team Members:
Florian Metze
Peter Bourgonje, Stefan Schaffer

Jitendra Ajmera
Funding by:
Deutsche Telekom Laboratories
See list of publications by Florian Metze and Jitendra Ajmera



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