14 research outputs found
Probing the Information Encoded in X-vectors
Deep neural network based speaker embeddings, such as x-vectors, have been
shown to perform well in text-independent speaker recognition/verification
tasks. In this paper, we use simple classifiers to investigate the contents
encoded by x-vector embeddings. We probe these embeddings for information
related to the speaker, channel, transcription (sentence, words, phones), and
meta information about the utterance (duration and augmentation type), and
compare these with the information encoded by i-vectors across a varying number
of dimensions. We also study the effect of data augmentation during extractor
training on the information captured by x-vectors. Experiments on the RedDots
data set show that x-vectors capture spoken content and channel-related
information, while performing well on speaker verification tasks.Comment: Accepted at IEEE Workshop on Automatic Speech Recognition and
Understanding (ASRU) 201
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge
International audienceThis report presents the system developed by the ABSP Laboratory team for the third DIHARD speech diarization challenge. Our main contribution in this work is to develop a simple and efficient solution for acoustic domain dependent speech diarization. We explore speaker embeddings for acoustic domain identification (ADI) task. Our study reveals that i-vector based method achieves considerably better performance than xvector based approach in the third DIHARD challenge dataset. Next, we integrate the ADI module with the diarization framework. The performance substantially improved over that of the baseline when we optimized the thresholds for agglomerative hierarchical clustering and the parameters for dimensionality reduction during scoring for individual acoustic domains. We achieved a relative improvement of 9.63% and 10.64% in DER for core and full conditions, respectively, for Track 1 of the DIHARD III evaluation set