Speaker normalization on conversational telephone speech
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Automatic speech recognition and speech activity detection in the CHIL smart room
MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction
Audio-visual technologies for lecture and meeting analysis inside smart rooms
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
The IBM Rich Transcription 2007 Speech-to-Text Systems for Lecture Meetings
Multimodal Technologies for Perception of Humans
The IBM RT07 Evaluation Systems for Speaker Diarization on Lecture Meetings
Multimodal Technologies for Perception of Humans
Simultaneous translation of lectures and speeches
Machine Translation
The IBM RT06s evaluation system for speech activity detection in CHIL seminars
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
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We describe the IBM systems submitted to the NIST RT06s Speech-to-Text (STT) evaluation campaign on the CHIL lecture meeting data for three conditions: Multiple distant microphone (MDM), single distant microphone (SDM), and individual headset microphone (IHM). The system building process is similar to the IBM conversational telephone speech recognition system. However, the best models for the far-field conditions (SDM and MDM) proved to be the ones that use neither variance normalization nor vocal tract length normalization. Instead, feature-space minimum-phone error discriminative training yielded the best results. Due to the relatively small amount of CHIL-domain data, the acoustic models of our systems are built on publicly available meeting corpora, with maximum a-posteriori adaptation applied twice on CHIL data during training: First, at the initial speaker-independent model, and subsequently at the minimum phone error model. For language modeling, we utilized meeting transcripts, text from scientific conference proceedings, and spontaneous telephone conversations. On development data, chosen in our work to be the 2005 CHIL-internal STT evaluation test set, the resulting language model provided a 4% absolute gain in word error rate (WER), compared to the model used in last year's CHIL evaluation. Furthermore, the developed STT system significantly outperformed our last year's results, by reducing close-talking microphone data WER from 36.9% to 25.4% on our development set. In the NIST RT06s evaluation campaign, both MDM and SDM systems scored well, however the IHM system did poorly due to unsuccessful cross-talk removal.