Speaker Detection System Research Articles

Large-Scale Speaker Diarization for Long Recordings and Small Collections

Performing speaker diarization of very long recordings is a problem for most diarization systems that are based on agglomerative clustering with an hidden Markov model (HMM) topology. Performing collection-wide speaker diarization, where each speaker is identified uniquely across the entire collection, is even a more challenging task. In this paper we propose a method with which it is possible to efficiently perform diarization of long recordings. We have also applied this method successfully to a collection of a total duration of approximately 15 hours. The method consists of first segmenting long recordings into smaller chunks on which diarization is performed. Next, a speaker detection system is used to link the speech clusters from each chunk and to assign a unique label to each speaker in the long recording or in the small collection. We show for three different audio collections that it is possible to perform high-quality diarization with this approach. The long meetings from the ICSI corpus are processed 5.5 times faster than the originally needed time and by uniquely labeling each speaker across the entire collection it becomes possible to perform speaker-based information retrieval with high accuracy (mean average precision of 0.57).

Approaches to Speaker Detection and Tracking in Conversational Speech

Dunn, Robert B., Reynolds, Douglas A., and Quatieri, Thomas F., Approaches to Speaker Detection and Tracking in Conversational Speech, Digital Signal Processing10(2000), 93–112.Two approaches to detecting and tracking speakers in multispeaker audio are described. Both approaches use an adapted Gaussian mixture model, universal background model (GMM-UBM) speaker detection system as the core speaker recognition engine. In one approach, the individual log-likelihood ratio scores, which are produced on a frame-by-frame basis by the GMM-UBM system, are used to first partition the speech file into speaker homogenous regions and then to create scores for these regions. We refer to this approach as internal segmentation. Another approach uses an external segmentationalgorithm, based on blind clustering, to partition the speech file into speaker homogenous regions. The adapted GMM-UBM system then scores each of these regions as in the single-speaker recognition case. We show that the external segmentation system outperforms the internal segmentation system for both detection and tracking. In addition, we show how different components of the detection and tracking algorithms contribute to the overall system performance.