26 accepted papers
To address the challenge of achieving both low drag and high maneuverability in complex water-filled pipeline environments, this study proposes a novel dual-spherical pipeline robot with integrated leak detection and mapping capabilities. A multi-objective optimization framework was established to s…
When exploring complex unknown environments, unmanned aerial vehicles (UAVs) often experience reduced efficiency and robustness due to unevenly distributed occlusions. This paper proposes an efficient hybrid autonomous exploration algorithm that adapts to environmental complexity, enabling effective…
Underwater and in-air environments exhibit distinct imaging characteristics, which should be carefully considered and effectively exploited for accurate depth estimation. In this work, we analyze the effectiveness of wavelength-dependent attenuation for underwater depth estimation and show that it i…
As machine learning (ML) application continues to expand across diverse fields, there is a rising demand for ML code generation. In this paper, we aim at a critical research question: Can machines autonomously generate ML code for sophisticated, human-designed algorithms or solutions? To answer this
In order to address the high complexity and low efficiency of amphibious propulsion systems, this paper proposes a novel variable wheel-propeller integrated mechanism for amphibious robots. By adjusting the blade pitch angle, it enables multiple motion modes, including rapid and stable movement on f…
Fabrics present significant challenges to robotic manipulation due to their complex dynamics and infinite degrees of freedom. This letter proposes a non-prehensile approach to aligning a fabric cut piece to a specified target pose, which is a common step for many garment manufacturing tasks. Compare
Protolanguage reconstruction is central to historical linguistics. The comparative method, one of the most influential theoretical and methodological frameworks in the history of the language sciences, allows linguists to infer protoforms (reconstructed ancestral words) from their reflexes (related…
The escalating demands for real-time scene comprehension in modern industries underscore the growing significance of semantic information in the daily tasks of robots, particularly in areas like autonomous inspection and target searching. This letter introduces a semantics-aware receding horizon pla
Existing work implementing comparative reconstruction of ancestral languages (proto-languages) has usually required full supervision. However, historical reconstruction models are only of practical value if they can be trained with a limited amount of labeled data. We propose a semisupervised histor…
Streaming recognition of multi-talker conversations has so far been evaluated only for 2-speaker single-turn sessions. In this paper, we investigate it for multi-turn meetings containing multiple speakers using the Streaming Unmixing and Recognition Transducer (SURT) model, and show that naively ext…
Streaming end-to-end multi-talker speech recognition aims at transcribing the overlapped speech from conversations or meetings with an all-neural model in a streaming fashion, which is fundamentally different from a modular-based approach that usually cascades the speech separation and the speech re…
The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal language model estimation (ILME) method [1]. In this method, the internal LM score is subtracted from the score obtained…
Recently, an end-to-end speaker-attributed automatic speech recognition (E2E SA-ASR) model was proposed as a joint model of speaker counting, speech recognition and speaker identification for monaural overlapped speech. In the previous study, the model parameters were trained based on the speaker-at…
In our previous research, we proposed a frame-level self-teaching network to regularize the deep neural network during training. In this paper, we extend the previous approach and propose a sequence self-teaching network to regularize the sequence-level information in speech recognition. The idea is…
This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior speech separation studies use pre-segmented audio signals, which are typically generated by mixing speech utterances on computers so that they fully overlap. Also, the separation algorithm…
Recently, the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition. However, RNN-T training is made difficult by the huge memory require…
Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity. However, in these models, the decisions to generate tokens are delayed compared to the actual acoustic boundaries since thei…
In our recent work, we proposed a layer trajectory long short-term memory (ltLSTM) model which decouples the tasks of temporal modeling and senone classification with time-LSTMs and depth-LSTMs. The ltLSTM model achieved significant accuracy improvement over the traditional multi-layer LSTM models f…
Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks (CNNs) can be used effectively instead of RNNs as…
In this paper, we investigate DCTNet for audio signal classification. Its output feature is related to Cohen's class of time-frequency distributions. We introduce the use of adaptive DCTNet (A-DCTNet) for audio signals feature extraction. The A-DCTNet applies the idea of constant-Q transform, with i…
Deep learning has significantly advanced state-of-the-art of speech recognition in the past few years. However, compared to conventional Gaussian mixture acoustic models, neural network models are usually much larger, and are therefore not very deployable in embedded devices. Previously, we investig…
We introduce multiplicative LSTM (mLSTM), a novel recurrent neural network architecture for sequence modelling that combines the long short-term memory (LSTM) and multiplicative recurrent neural network architectures. mLSTM is characterised by its ability to have different recurrent transition funct…
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…
Recently, there has been an increasing interest in end-to-end speech recognition using neural networks, with no reliance on hidden Markov models (HMMs) for sequence modelling as in the standard hybrid framework. The recurrent neural network (RNN) encoderdecoder is such a model, performing sequence t…
Long Short-Term Memory (LSTM) is a particular type of recurrent neural network (RNN) that can model long term temporal dynamics. Recently it has been shown that LSTM-RNNs can achieve higher recognition accuracy than deep feed-forword neural networks (DNNs) in acoustic modelling. However, speaker ada…
Acoustic models based on Gaussian mixture models (GMMs) typically use short span acoustic feature inputs. This does not capture long-term temporal information from speech owing to the conditional independence assumption of hidden Markov models. In this paper, we present an implicit approach that app…