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Automatic Speech Recognition

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TitleStatusHype
Late fusion ensembles for speech recognition on diverse input audio representations—0
Latency-Controlled Neural Architecture Search for Streaming Speech Recognition—0
Latent Dirichlet Allocation Based Organisation of Broadcast Media Archives for Deep Neural Network Adaptation—0
Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition—0
Lattention: Lattice-attention in ASR rescoring—0
Lattice-based Improvements for Voice Triggering Using Graph Neural Networks—0
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers—0
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models—0
Lattice Transformer for Speech Translation—0
Layer Pruning on Demand with Intermediate CTC—0
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition—0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech—0
Learning a Dual-Mode Speech Recognition Model via Self-Pruning—0
Learning a Neural Diff for Speech Models—0
Learning ASR pathways: A sparse multilingual ASR model—0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition—0
Learning Language Structures through Grounding—0
Learning linearly separable features for speech recognition using convolutional neural networks—0
Learning not to Discriminate: Task Agnostic Learning for Improving Monolingual and Code-switched Speech Recognition—0
Learning Robust Dialog Policies in Noisy Environments—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Learning Similarity Functions for Pronunciation Variations—0
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition—0
Learning to Jointly Transcribe and Subtitle for End-to-End Spontaneous Speech Recognition—0
Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition—0
Learning When to Trust Which Teacher for Weakly Supervised ASR—0
Learning without Forgetting: Task Aware Multitask Learning for Multi-Modality Tasks—0
Learning Word-Level Confidence For Subword End-to-End ASR—0
Learning Word-Like Units from Joint Audio-Visual Analysis—0
Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data—0
LegoNN: Building Modular Encoder-Decoder Models—0
Less Is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding—0
Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR—0
Leveraging Data Collection and Unsupervised Learning for Code-switched Tunisian Arabic Automatic Speech Recognition—0
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise—0
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxóchitl Mixtec—0
Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yol\'oxochitl Mixtec—0
Leveraging End-to-End Speech Recognition with Neural Architecture Search—0
Leveraging Language ID to Calculate Intermediate CTC Loss for Enhanced Code-Switching Speech Recognition—0
Leveraging Large Language Models for Exploiting ASR Uncertainty—0
Leveraging Large Text Corpora for End-to-End Speech Summarization—0
Harnessing Indirect Training Data for End-to-End Automatic Speech Translation: Tricks of the Trade—0
Leveraging Pre-trained Language Model for Speech Sentiment Analysis—0
Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection—0
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition—0
Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions—0
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