Investigation into Target Speaking Rate Adaptation for Voice Conversion - Kuhlmann, Michael; Seebauer, Fritz; Ebbers, Janek; Wagner, Petra; Haeb-Umbach, Reinhold (Interspeech 2022)

Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription - Gburrek, Tobias; Schmalenstroeer, Joerg; Heitkaemper, Jens; Haeb-Umbach, Reinhold (2022 International Workshop on Acoustic Signal Enhancement (IWAENC))

A Meeting Transcription System for an Ad-Hoc Acoustic Sensor Network - Gburrek, Tobias; Boeddeker, Christoph; von Neumann, Thilo; Cord-Landwehr, Tobias; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold

Threshold Independent Evaluation of Sound Event Detection Scores - Ebbers, Janek; Haeb-Umbach, Reinhold; Serizel, Romain (Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))
Performing an adequate evaluation of sound event detection (SED) systems is far from trivial and is still subject to ongoing research. The recently proposed polyphonic sound detection (PSD)-receiver operating characteristic (ROC) and PSD score (PSDS) make an important step into the direction of an evaluation of SED systems which is independent from a certain decision threshold. This allows to obtain a more complete picture of the overall system behavior which is less biased by threshold tuning. Yet, the PSD-ROC is currently only approximated using a finite set of thresholds. The choice of the thresholds used in approximation, however, can have a severe impact on the resulting PSDS. In this paper we propose a method which allows for computing system performance on an evaluation set for all possible thresholds jointly, enabling accurate computation not only of the PSD-ROC and PSDS but also of other collar-based and intersection-based performance curves. It further allows to select the threshold which best fulfills the requirements of a given application. Source code is publicly available in our SED evaluation package sed_scores_eval.

Far-Field Automatic Speech Recognition - Haeb-Umbach, Reinhold; Heymann, Jahn; Drude, Lukas; Watanabe, Shinji; Delcroix, Marc; Nakatani, Tomohiro (Proceedings of the IEEE)
The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions and, consequently, quite different processing pipelines have emerged compared to ASR for close-talk speech. A signal enhancement front-end for dereverberation, source separation and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multi-condition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.

End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend - Zhang, Wangyou; Boeddeker, Christoph; Watanabe, Shinji; Nakatani, Tomohiro; Delcroix, Marc; Kinoshita, Keisuke; Ochiai, Tsubasa; Kamo, Naoyuki; Haeb-Umbach, Reinhold; Qian, Yanmin (ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

ESPnet-SE: End-To-End Speech Enhancement and Separation Toolkit Designed for ASR Integration - Li, Chenda; Shi, Jing; Zhang, Wangyou; Subramanian, Aswin Shanmugam; Chang, Xuankai; Kamo, Naoyuki; Hira, Moto; Hayashi, Tomoki; Boeddeker, Christoph; Chen, Zhuo; Watanabe, Shinji (2021 IEEE Spoken Language Technology Workshop (SLT))

Dual-Path RNN for Long Recording Speech Separation - Li, Chenda; Luo, Yi; Han, Cong; Li, Jinyu; Yoshioka, Takuya; Zhou, Tianyan; Delcroix, Marc; Kinoshita, Keisuke; Boeddeker, Christoph; Qian, Yanmin; Watanabe, Shinji; Chen, Zhuo (2021 IEEE Spoken Language Technology Workshop (SLT))

A Database for Research on Detection and Enhancement of Speech Transmitted over HF links - Heitkaemper, Jens; Schmalenstroeer, Joerg; Ion, Valentin; Haeb-Umbach, Reinhold (Speech Communication; 14th ITG-Symposium)

A Comparison and Combination of Unsupervised Blind Source Separation Techniques - Boeddeker, Christoph; Rautenberg, Frederik; Haeb-Umbach, Reinhold (ITG Conference on Speech Communication)
Unsupervised blind source separation methods do not require a training phase and thus cannot suffer from a train-test mismatch, which is a common concern in neural network based source separation. The unsupervised techniques can be categorized in two classes, those building upon the sparsity of speech in the Short-Time Fourier transform domain and those exploiting non-Gaussianity or non-stationarity of the source signals. In this contribution, spatial mixture models which fall in the first category and independent vector analysis (IVA) as a representative of the second category are compared w.r.t. their separation performance and the performance of a downstream speech recognizer on a reverberant dataset of reasonable size. Furthermore, we introduce a serial concatenation of the two, where the result of the mixture model serves as initialization of IVA, which achieves significantly better WER performance than each algorithm individually and even approaches the performance of a much more complex neural network based technique.