Speaker-Aware Neural Network Based Beamformer for Speaker Extraction in Speech Mixtures

By Kateřina Žmolíková, Marc Delcroix, Keisuke Kinoshita, Takuya Higuchi, Atsunori Ogawa, Tomohiro Nakatani. 2017

112 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shinji Watanabe
2. Jinyu Li
3. Dong Yu
4. Yossi Adi
5. Lior Wolf
6. John R. Hershey
7. Zhuo Chen
8. Yoshua Bengio

In Signal Processing

1. Shinji Watanabe
2. Jinyu Li
3. Dong Yu
4. Yossi Adi
5. John R. Hershey
6. Zhuo Chen
7. Lior Wolf
8. Marc Delcroix

Citations in the corpus

• Voice Separation with an Unknown Number of Multiple Speakers (2020, methodology): “In (Zmolikova et al., 2017; Delcroix et al., 2018) a neural network employed estimated i-vectors (Dehak et al.”
• Neural Target Speech Extraction: An overview (2023, methodology): “These correlation matrices can be computed from the observed signal and the time-frequency mask estimated by the TSE system [30].”
• Continuous Speech Separation: Dataset and Analysis (2020, background): “Use of visual information [9, 10], audio snippets of the speakers [11, 12, 13], or their locations [14, 15, 16] were investigated.”
• VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking (2018, methodology): “While training the VoiceFilter system, the input audios are divided into segments of 3 seconds each and are converted, if necessary, to single channel audios with a sampling rate of 16 kHz.”
• Single Channel Target Speaker Extraction and Recognition with Speaker Beam (2018, background): “In [16], we showed that a key to achieve high target speaker extraction performance was to employ a context adaptive DNN (CADNN) architecture proposed for speaker adaptation of the acoustic model [17], which can adjust its parameters depending on auxiliary features representing the target speaker ch”
• Acoustic Modeling for Distant Multi-talker Speech Recognition with Single- and Multi-channel Branches (2019, methodology): “We internally evaluated deep clustering [19], permutation invariant training [20], chimera++ networks [21], and a speakeraware neural beamformer [22, 23], but none of them successfully improved the accuracy for the CHiME-5 dataset.”
• SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures (2019, methodology): “The work in [28] focused mainly on a closed-speaker-set case and evaluated a factorized layer scheme (see Section III) as the most suitable method.”