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Contains fulltext : 208491.pdf (publisher's version ) (Open Access)

We investigate the use of deep neural networks (DNNs) for the speaker diarization task to improve performance under domain mismatched conditions. Three unsupervised domain adaptation techniques, namely inter-dataset variability compensation (IDVC), domain-invariant covariance normalization (DICN), and domain mismatch modeling (DMM), are applied on DNN based speaker embeddings to compensate for the mismatch in the embedding subspace. We present results conducted on the DIHARD data, which was released for the 2018 diarization challenge. Collected from a diverse set of domains, this data provides very challenging domain mismatched conditions for the diarization task. Our results provide insights into how the performance of our proposed system could be further improved.

With the increasing use of a high quality acoustic device to monitor wildlife population, it has become imperative to develop techniques for analyzing animals’ calls automatically. Bird sound detection is one example of a long-term monitoring project where data are collected in continuous periods, often covermultiple sites at the same time. Inspired by the success of deep learning approaches in various audio classification tasks, this paper first reviews previous works exploiting deep learning for bird audio detection, and then proposes a novel 3-dimensional (3D) convolutional and recurrent neural networks. We propose 3D convolutions for extracting long-term and short-term information in frequency simultaneously. In order to leverage powerful and compact features of 3D convolution, we employ separate recurrent neural networks (RNN), acting on each filter of the last convolutional layers rather than stacking the feature maps in the typical combined convolution and recurrent architectures. Our best model achieved a preview of 88.70% Area Under ROC Curve (AUC) score on the unseen evaluation data in the second edition of bird audio detection challenge. Further improvement with model adaptation led to a 89.58% AUC score.

The subject of this paper is the conversion of a given speaker's voice (the source speaker) into another identified voice (the target one). We assume we have at our disposal a large amount of speech samples from source and target voice with at least a part of them being parallel. The proposed system is built on a mapping function between source and target spectral envelopes followed by a frame selection algorithm to produce final spectral envelopes. Converted speech is produced by a basic LP analysis of the source and LP synthesis using the converted spectral envelopes. We compared three types of conversion: without mapping, with mapping and using the excitation of the source speaker and finally with mapping using the excitation of the target. Results show that the combination of mapping and frame selection provide the best results, and underline the interest to work on methods to convert the LP excitation., QC 20161031