No abstract

No abstract

Recently, several end-to-end speaker verification systems based on deep neural networks (DNNs) have been proposed. These systems have been proven to be competitive for text-dependent tasks as well as for text-independent tasks with short utterances. However, for text-independent tasks with longer utterances, end-to-end systems are still outperformed by standard i-vector + PLDA systems. In this work, we develop an end-to-end speaker verification system that is initialized to mimic an i-vector + PLDA baseline. The system is then further trained in an end-to-end manner but regularized so that it does not deviate too far from the initial system. In this way we mitigate overfitting which normally limits the performance of endto-end systems. The proposed system outperforms the i-vector + PLDA baseline on both long and short duration utterances.

This paper presents the approach developed by the BUT team for the first DIHARD speech diarization challenge, which is based on our Bayesian Hidden Markov Model with eigenvoice priors system. Besides the description of the approach, we provide a brief analysis of different techniques and data processing methods tested on the development set. We also introduce a simple attempt for overlapped speech detection that we used for attaining cleaner speaker models and reassigning overlapped speech to multiple speakers. Finally, we present results obtained on the evaluation set and discuss findings we made during the development phase and with the help of the DIHARD leaderboard feedback.

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging unlabelled utterances, due to the classification loss over training speakers. In this paper, we explore an alternative training strategy to enable the use of unlabelled utterances in training. We propose to train speaker embedding extractors via reconstructing the frames of a target speech segment, given the inferred embedding of another speech segment of the same utterance. We do this by attaching to the standard speaker embedding extractor a decoder network, which we feed not merely with the speaker embedding, but also with the estimated phone sequence of the target frame sequence.The reconstruction loss can be used either as a single objective, or be combined with the standard speaker classification loss. In the latter case, it acts as a regularizer, encouraging generalizability to speakers unseen during training. In all cases, the proposed architectures are trained from scratch and in an endto-end fashion. We demonstrate the benefits from the proposed approach on VoxCeleb and Speakers in the wild, and we report notable improvements over the baseline.