Filtering the time sequences of spectral parameters for speech recognition
Speech Communication
Time and frequency filtering of filter-bank energies for robust HMM speech recognition
Speech Communication - Special issue on noise robust ASR
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SVM Training Time Reduction using Vector Quantization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Segregation of speakers for speech recognition and speaker identification
ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference
Robust Speaker Identification for Meetings: UPC CLEAR'07 Meeting Room Evaluation System
Multimodal Technologies for Perception of Humans
Tuning-robust initialization methods for speaker diarization
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper the authors present the UPC speaker diarization system for the NIST Rich Transcription Evaluation (RT07s) [1] conducted on the conference environment. The presented system is based on the ICSI RT06s system, which employs agglomerative clustering with a modified Bayesian Criterion (BIC) measure to decide which pairs of clusters to merge and to determine when to stop merging clusters [2]. This is the first participation of the UPC in the RT Speaker Diarization Evaluation and the purpose of this work has been the consolidation of a baseline system which can be used in the future for further research in the field of diarization. We have introduced, as prior modules before the diarization system, an Speech/Non-Speech detection module based on a Support Vector Machine from UPC and a Wiener Filtering from an implementation of the QIO front-end. In the speech parameterization a Frequency Filtering (FF) of the filter-bank energies is applied instead the classical Discrete Cosine Transform in the Mel-Cepstrum analysis. In addition, it is introduced a small changes in the complexity selection algorithm and a new post-processing technique which process the shortest clusters at the end of each Viterbi segmentation.