6 accepted papers
End-to-end neural diarization (EEND) methods use just a single neural network. EEND-TA, a recently proposed EEND technique, performs diarization for a flexible number of speakers in a non-autoregressive manner. In this paper, we explore combining EEND-TA with Variance-Covariance Regularization (VCRe…
Autoregressive (AR) automatic speech recognition (ASR) models predict each output token conditioning on the previous ones, which slows down their inference speed. On the other hand, non-autoregressive (NAR) models predict tokens independently and simultaneously within a constant number of decoding i…
Self-attention has become an important component for end-to-end (E2E) automatic speech recognition (ASR). Recently, Convolution-augmented Transformer (Conformer) with relative positional encoding (RPE) achieved state-of-the-art performance. However, the computational and memory complexity of self-at…
Factorized Hidden Layer (FHL) has been proposed for the adaptation of deep neural network (DNN) and Long Short-Term Memory (LSTM) based acoustic models (AMs). In FHL, a speaker-dependent (SD) transformation matrix and an SD bias are included in addition to the standard affine transformation. The SD…
Subspace methods are used for deep neural network (DNN)-based acoustic model adaptation. These methods first construct a subspace and then perform the speaker adaptation as a point in the subspace. This paper aims to investigate the effectiveness of subspace methods for robust unsupervised adaptatio…
In automatic speech recognition (ASR), adaptation and adaptive training techniques are used to perform speaker normalization. Previous methods mainly focus on using these techniques in isolation. In contrast, this paper investigates two approaches to improve the ASR performance by combining i-vector…