16 accepted papers
Multimodal Retrieval-Augmented Generation (MM-RAG) has emerged as a promising approach for enhancing the reliability and factuality of large vision-language models (LVLMs). While end-to-end loss backpropagation is infeasible due to non-differentiable operations during the forward process, current me
The residual stream acts as a memory bus where transformer layers both store and access features (Elhage et al., 2021). We consider changing the mechanism for retrieving and storing information in the residual stream, and replace the residual stream of the transformer with an outer product memory ma…
This paper introduces TVB-HKSL-News, a new Hong Kong sign language (HKSL) dataset collected from a TV news program over a period of 7 months. The dataset is collected to enrich resources for HKSL and support research in large-vocabulary continuous sign language recognition (SLR) and translation (SLT…
"The objective of this paper is to develop a functional system for translating spoken languages into sign languages, referred to as Spoken2Sign translation. The Spoken2Sign task is orthogonal and complementary to traditional sign language to spoken language (Sign2Spoken) translation. To enable Spoke…
Research on continuous sign language recognition (CSLR) is essential to bridge the communication gap between deaf and hearing individuals. Numerous previous studies have trained their models using the connectionist temporal classification (CTC) loss. During inference, these CTC-based models generall…
Sign languages are visual languages which convey information by signers' handshape, facial expression, body movement, and so forth. Due to the inherent restriction of combinations of these visual ingredients, there exist a significant number of visually indistinguishable signs (VISigns) in sign lang…
Most lip-to-speech (LTS) synthesis models are trained and evaluated with the assumption that the audio-video pairs in the dataset are well synchronized. In this work, we demonstrate that commonly used audiovisual datasets such as GRID, TCD-TIMIT, and Lip2Wav can, however, have the data asynchrony is…
Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substanti…
With the global population ageing rapidly, Alzheimer's disease (AD) is particularly prominent in older adults, which has an insidious onset followed by gradual, irreversible deterioration in cognitive domains (memory, communication, etc). Thus the detection of Alzheimer's disease is crucial for time…
In this paper, we present a simple but novel framework to train a nonparallel many-to-many voice conversion (VC) model based on the encoder-decoder architecture. It is observed that an encoder-decoder text-to-speech (TTS) model and an encoder-decoder VC model have the same structure. Thus, we propos…
In this paper we propose orthogonal training schemes to improve the effectiveness of cosine similarity measurements in text-independent speaker verification (SV) tasks. Compared to the PLDA backend, cosine similarity is simple to compute, and it does not require extra data or time to build a separat…
In this paper, we propose novel stochastic modeling of various components of a continuous sign language recognition (CSLR) system that is based on the transformer encoder and connectionist temporal classification (CTC). Most importantly, We model each sign gloss with multiple states, and the number…
Lip-reading is the task of recognizing speech solely from the visual movement of the mouth. Although recent works have demonstrated the effectiveness of convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network in lip-reading, similar architectures under low-resou…
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…
Deep neural network has obtained significant accuracy improvement in many large vocabulary continuous speech recognition (LVCSR) tasks. Recently, it was shown that even better performance can be obtained by modeling a larger number of more discriminative senones. However, as the neural network becom…