4 research outputs found
Speaker detection in the wild: Lessons learned from JSALT 2019
Submitted to ICASSP 2020This paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tackle a wide range of conditions that go from meetings to wild speech. We describe the research threads we explored and a set of modules that was successful for these scenarios. The ultimate goal was to explore speaker detection; but our first finding was that an effective diarization improves detection, and not having a diarization stage impoverishes the performance. All the different configurations of our research agree on this fact and follow a main backbone that includes diarization as a previous stage. With this backbone, we analyzed the following problems: voice activity detection, how to deal with noisy signals, domain mismatch, how to improve the clustering; and the overall impact of previous stages in the final speaker detection. In this paper, we show partial results for speaker diarizarion to have a better understanding of the problem and we present the final results for speaker detection
Speaker detection in the wild: Lessons learned from JSALT 2019
International audienceThis paper presents the problems and solutions addressed at the JSALT workshop when using a single microphone for speaker detection in adverse scenarios. The main focus was to tackle a wide range of conditions that go from meetings to wild speech. We describe the research threads we explored and a set of modules that was successful for these scenarios. The ultimate goal was to explore speaker detection; but our first finding was that an effective diarization improves detection, and not having a diarization stage impoverishes the performance. All the different configurations of our research agree on this fact and follow a main backbone that includes diarization as a previous stage. With this backbone, we analyzed the following problems: voice activity detection, how to deal with noisy signals, domain mismatch, how to improve the clustering; and the overall impact of previous stages in the final speaker detection. In this paper, we show partial results for speaker diarizarion to have a better understanding of the problem and we present the final results for speaker detection
Latent Iterative Refinement for Modular Source Separation
Traditional source separation approaches train deep neural network models
end-to-end with all the data available at once by minimizing the empirical risk
on the whole training set. On the inference side, after training the model, the
user fetches a static computation graph and runs the full model on some
specified observed mixture signal to get the estimated source signals.
Additionally, many of those models consist of several basic processing blocks
which are applied sequentially. We argue that we can significantly increase
resource efficiency during both training and inference stages by reformulating
a model's training and inference procedures as iterative mappings of latent
signal representations. First, we can apply the same processing block more than
once on its output to refine the input signal and consequently improve
parameter efficiency. During training, we can follow a block-wise procedure
which enables a reduction on memory requirements. Thus, one can train a very
complicated network structure using significantly less computation compared to
end-to-end training. During inference, we can dynamically adjust how many
processing blocks and iterations of a specific block an input signal needs
using a gating module