8 accepted papers
Self-supervised learning (SSL) has attracted increased attention for learning meaningful speech representations. Speech SSL models, such as WavLM, employ masked prediction training to encode general-purpose representations. In contrast, speaker SSL models, exemplified by DINO-based models, adopt utt…
Crowd-sourcing has attracted much attention due to its growing importance to society, and numerous studies have been conducted on task allocation and wage determination. Recent works have focused on optimizing task allocation and workers' wages, simultaneously. However, existing methods do not provi…
In this paper, we tackle the Bayesian estimation of point process intensity as a function of covariates. We propose a novel augmentation of permanental process called augmented permanental process, a doubly-stochastic point process that uses a Gaussian process on covariate space to describe the Baye…
This paper describes neural ConfNet classification, a novel fully neural network based spoken utterance classification method that uses word confusion networks (ConfNets). Our motivation is to establish a spoken utterance classification method that can precisely understand natural language and robus…
Neural word vector (NWV) such as word2vec is a powerful text representation tool that can encode extensive semantic information into compact vectors. This ability poses an interesting question in relation to image processing research - Can we learn better semantic image features from NWVs? We empiri…
Adapting acoustic models to speakers have shown to greatly improve performance for many tasks. Among the adaptation approaches, exploiting auxiliary features characterizing speakers or environments has received great attention because they allow rapid adaptation, i.e. adaptation with limited amount…
Constructing deep neural network (DNN) acoustic models from limited training data is an important issue for the development of automatic speech recognition (ASR) applications that will be used in various application-specific acoustic environments. To this end, domain adaptation techniques that train…
Parallel phonetically aware deep neural networks (PPA-DNNs) and long short-term memory recurrent neural networks (PPA-LSTM-RNNs) to enhance frame-by-frame discriminative modeling of spoken language identification are proposed. This idea is inspired by traditional systems based on parallel phoneme re…