12 citations · 18 across the 6 of their papers we have counts for
9 papers
Diminishing Domain Mismatch for DNN-Based Acoustic Distance Estimation via Stochastic Room Reverberation Models
Tobias Gburrek, Adrian Meise, Joerg Schmalenstroeer +1
The room impulse response (RIR) encodes, among others, information about the distance of an acoustic source from the sensors. Deep neural networks (DNNs) have been shown to be able…
Mixture Encoder for Joint Speech Separation and Recognition
Simon Berger, Peter Vieting, Christoph Boeddeker +2
Multi-speaker automatic speech recognition (ASR) is crucial for many real-world applications, but it requires dedicated modeling techniques. Existing approaches can be divided into…
An Investigation into the Effectiveness of Enhancement in ASR Training and Test for CHiME-5 Dinner Party Transcription
Catalin Zorila, Christoph Boeddeker, Rama Doddipatla +1
Despite the strong modeling power of neural network acoustic models, speech enhancement has been shown to deliver additional word error rate improvements if multi-channel data is a…
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
Naoyuki Kanda, Christoph Boeddeker, Jens Heitkaemper +4
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for…
Unsupervised training of neural mask-based beamforming
Lukas Drude, Jahn Heymann, Reinhold Haeb-Umbach
We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood crite…
Unsupervised training of a deep clustering model for multichannel blind source separation
Lukas Drude, Daniel Hasenklever, Reinhold Haeb-Umbach
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an…