13 accepted papers
The success of large language models in text processing has inspired their adaptation to speech modeling. However, since speech is continuous and complex, it is often discretized for autoregressive modeling. Speech tokens derived from self-supervised models (known as semantic tokens) typically focus…
Speech foundation models, such as HuBERT and its variants, are pre-trained on large amounts of unlabeled speech data and then used for a range of downstream tasks. These models use a masked prediction objective, where the model learns to predict information about masked input segments from the unmas…
Iterative self-training, or iterative pseudo-labeling (IPL)—using an improved model from the current iteration to provide pseudo-labels for the next iteration—has proven to be a powerful approach to enhance the quality of speaker representations. Recent applications of IPL in unsupervised speaker re…
This study explores using embedding rank as an unsupervised evaluation metric for general-purpose speech encoders trained via self-supervised learning (SSL). Traditionally, assessing the performance of these encoders is resource-intensive and requires labeled data from the downstream tasks. Inspired…
We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weight parameters, so-called data parameters, for target classes and instances are introduced and optimized along with model…
Deep attractor networks (DANs) are a recently introduced method to blindly separate sources from spectral features of a monaural recording using bidirectional long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is inherently not online-ready and resorting to operating on bloc…
Beamforming approaches using time-frequency masks have recently been investigated and have shown promising results for noise robust automatic speech recognition (ASR) in many tasks. The time-frequency masks are estimated to compute the spatial statistics of target speech and noise signals, and then…
This paper addresses the problem of recognizing speech corrupted by overlapping speakers in a multichannel setting. To extract a target speaker from the mixture, we use a neural network based beamformer which uses masks estimated by a neural network to compute statistically optimal spatial filters.…
We propose a novel framework designed to extend conventional deep neural network (DNN)-based feature enhancement approaches. In general, the conventional DNN-based feature enhancement framework aims to map input noisy observation to clean speech or a binary/ soft mask in a deterministic way, assumin…
Recently, time-frequency mask-based beamforming has been extensively studied as the frontend of deep neural network (DNN) based automatic speech recognition (ASR) in noisy environments. Two mask estimation approaches have been separately developed for this beamforming method, namely the the DNN-base…
In this paper, we perform beamforming with a speech recognition-level criterion. A beamformer is usually designed by optimizing signal-level criteria, e.g., by minimizing the beamformer output covariance or by maximizing the signal-to-noise ratio (SNR). Such signal-level criteria do not always guara…
This paper considers acoustic beamforming for noise robust automatic speech recognition (ASR). A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise r…
This paper addresses a minimum variance distortionless response (MVDR) beamforming based speech enhancement approach for meeting speech recognition. In a meeting situation, speaker overlaps and noise signals are not negligible. To handle these issues, we employ MVDR beamforming, where accurate estim…