LibriWASN: A Data Set for Meeting Separation, Diarization, and Recognition with Asynchronous Recording Devices

We present LibriWASN, a data set whose design follows closely the LibriCSS meeting recognition data set, with the marked difference that the data is recorded with devices that are randomly positioned on a meeting table and whose sampling clocks are not synchronized. Nine different devices, five smartphones with a single recording channel and four microphone arrays, are used to record a total of 29 channels. Other than that, the data set follows closely the LibriCSS design: the same LibriSpeech sentences are played back from eight loudspeakers arranged around a meeting table and the data is organized in subsets with different percentages of speech overlap. LibriWASN is meant as a test set for clock synchronization algorithms, meeting separation, diarization and transcription systems on ad-hoc wireless acoustic sensor networks. Due to its similarity to LibriCSS, meeting transcription systems developed for the former can readily be tested on LibriWASN. The data set is recorded in two different rooms and is complemented with ground-truth diarization information of who speaks when.Comment: Accepted for presentation at the ITG conference on Speech Communication 202

Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion

Gburrek T, Ebbers J, Häb-Umbach R, Wagner P. Unsupervised Learning of a Disentangled Speech Representation for Voice Conversion. In: Proceedings of the 10 Speech Synthesis Workshop (SSW10). 2019.This paper presents an approach to voice conversion, which does neither require parallel data nor speaker or phone labels for training. It can convert between speakers which are not in the training set by employing the previously proposed concept of a factorized hierarchical variational autoencoder. Here, linguistic and speaker induced variations are separated upon the notion that content induced variations change at a much shorter time scale, i.e., at the segment level, than speaker induced variations, which vary at the longer utterance level. In this contribution we propose to employ convolutional instead of recurrent network layers in the encoder and decoder blocks, which is shown to achieve better phone recognition accuracy on the latent segment variables at frame-level due to their better temporal resolution. For voice conversion the mean of the utterance variables is replaced with the respective estimated mean of the target speaker. The resulting log-mel spectra of the decoder output are used as local conditions of a WaveNet which is utilized for synthesis of the speech waveforms. Experiments show both good disentanglement properties of the latent space variables, and good voice conversion performance, as assessed both quantitatively and qualitatively