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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: | 07/2007-12/2008 |
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T-labs Team Members: | Florian Metze |
Students: | Peter Bourgonje, Stefan Schaffer |
Partners: | Jitendra Ajmera |
Funding by: | Deutsche Telekom Laboratories |
Publications: | See list of publications by Florian Metze and Jitendra Ajmera |