[HTML][HTML] Large language models in law: A survey
The advent of artificial intelligence (AI) has significantly impacted the traditional judicial
industry. Moreover, recently, with the development of the concept of AI-generated content …
industry. Moreover, recently, with the development of the concept of AI-generated content …
Deep learning for environmentally robust speech recognition: An overview of recent developments
Eliminating the negative effect of non-stationary environmental noise is a long-standing
research topic for automatic speech recognition but still remains an important challenge …
research topic for automatic speech recognition but still remains an important challenge …
Adversarial examples: Attacks and defenses for deep learning
With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …
learning is being applied in many safety-critical environments. However, deep neural …
The Microsoft 2017 conversational speech recognition system
We describe the latest version of Microsoft's conversational speech recognition system for
the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to …
the Switchboard and CallHome domains. The system adds a CNN-BLSTM acoustic model to …
Permutation invariant training of deep models for speaker-independent multi-talker speech separation
We propose a novel deep learning training criterion, named permutation invariant training
(PIT), for speaker independent multi-talker speech separation, commonly known as the …
(PIT), for speaker independent multi-talker speech separation, commonly known as the …
[PDF][PDF] Purely sequence-trained neural networks for ASR based on lattice-free MMI.
In this paper we describe a method to perform sequencediscriminative training of neural
network acoustic models without the need for frame-level cross-entropy pre-training. We use …
network acoustic models without the need for frame-level cross-entropy pre-training. We use …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Achieving human parity in conversational speech recognition
Conversational speech recognition has served as a flagship speech recognition task since
the release of the Switchboard corpus in the 1990s. In this paper, we measure the human …
the release of the Switchboard corpus in the 1990s. In this paper, we measure the human …
DeepDefense: identifying DDoS attack via deep learning
Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal
threats to the Internet. Automatically detecting DDoS attack packets is one of the main …
threats to the Internet. Automatically detecting DDoS attack packets is one of the main …
English conversational telephone speech recognition by humans and machines
One of the most difficult speech recognition tasks is accurate recognition of human to human
communication. Advances in deep learning over the last few years have produced major …
communication. Advances in deep learning over the last few years have produced major …