18 accepted papers
We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with simulated room impulse responses (RIRs) using omnidirectional microphones without needing to collect real RIRs. By relying…
Augmented reality (AR) glasses have an immense potential for enhancing conversations by leveraging speech recognition to display real-time transcription or translation, for example, to assist people with hearing impairments or for people conversing in a non-native language. For deployment in real en…
High quality speech capture has been widely studied for both voice communication and human computer interface reasons. To improve the capture performance, we can often find multi-microphone speech enhancement techniques deployed on various devices. Multi-microphone speech enhancement problem is ofte…
The recently-proposed mixture invariant training (MixIT) is an unsupervised method for training single-channel sound separation models because it does not require ground-truth isolated reference sources. In this paper, we investigate using MixIT to adapt a separation model on real far-field overlapp…
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. End-to-end diarization models have the advantage of handling speaker overlap and enabling straightforward handling of discriminative training, unlike traditional clustering-based diari…
We propose a benchmark of state-of-the-art sound event detection systems (SED). We design synthetic evaluation sets to focus on specific sound event detection challenges. We analyze the performance of the submissions to DCASE 2020 Task 4 as a function of time-related modifications (time position of…
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types. The dataset consists of 23 hours of single-source audio data drawn from 357 classes, which are used to create mixtur…
Time-domain audio separation networks based on dilated temporal convolutions have recently been shown to perform very well compared to methods that are based on a time-frequency representation in speech separation tasks, even outperforming an oracle binary time-frequency mask of the speakers. This p…
In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sou…
Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech segment. A separated, or cleaned, version of e…
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was developed to give researchers worldwide a way to evaluate the quality of their algorithms in a simple, fair, and hopefully insi…
Neural network-based speech separation has received a surge of interest in recent years. Previously proposed methods either are speaker independent or extract a target speaker's voice by using his or her voice snippet. In applications such as home devices or office meeting transcriptions, a possible…
This work examines acoustic beamformers employing neural networks (NNs) for mask prediction as front -end for automatic speech recognition (ASR) systems for practical scenarios like voice-enabled home devices. To test the versatility of the mask predicting network, the system is evaluated with diffe…
This paper describes a neural network approach to far-field speech separation using multiple microphones. Our proposed approach is speaker-independent and can learn to implicitly figure out the number of speakers constituting an input speech mixture. This is realized by utilizing the permutation inv…
Far-field speech recognition in noisy and reverberant conditions remains a challenging problem despite recent deep learning breakthroughs. This problem is commonly addressed by acquiring a speech signal from multiple microphones and performing beamforming over them. In this paper, we propose to use…
Despite the significant progress in speech recognition enabled by deep neural networks, poor performance persists in some scenarios. In this work, we focus on far-field speech recognition which remains challenging due to high levels of noise and reverberation in the captured speech signals. We propo…
This paper investigates the application of the probabilistic linear discriminant analysis (PLDA) to speaker diarization of telephone conversations. We introduce using a variational Bayes (VB) approach for inference under a PLDA model for modelling segmental i-vectors in speaker diarization. Determin…
Separation of speech embedded in non-stationary interference is a challenging problem that has recently seen dramatic improvements using deep network-based methods. Previous work has shown that estimating a masking function to be applied to the noisy spectrum is a viable approach that can be improve…