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Automatic Speech Recognition

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Showing 801–850 of 3174 papers

TitleStatusHype
Audio Adversarial Examples for Robust Hybrid CTC/Attention Speech Recognition—0
Critical Appraisal of Artificial Intelligence-Mediated Communication—0
Creating Spoken Dialog Systems in Ultra-Low Resourced Settings—0
A two-step approach to leverage contextual data: speech recognition in air-traffic communications—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
CR-CTC: Consistency regularization on CTC for improved speech recognition—0
CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments—0
A two-stage transliteration approach to improve performance of a multilingual ASR—0
Learning Speech Representation From Contrastive Token-Acoustic Pretraining—0
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition—0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers—0
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition—0
Cross-lingual studies of ASR errors: paradigms for perceptual evaluations—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection—0
CPPF: A contextual and post-processing-free model for automatic speech recognition—0
All-neural online source separation, counting, and diarization for meeting analysis—0
Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR—0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications—0
CS-Dialogue: A 104-Hour Dataset of Spontaneous Mandarin-English Code-Switching Dialogues for Speech Recognition—0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
CTC-Assisted LLM-Based Contextual ASR—0
2-bit Conformer quantization for automatic speech recognition—0
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving—0
CTC Variations Through New WFST Topologies—0
Cumulative Adaptation for BLSTM Acoustic Models—0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 2020—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
Attentive Adversarial Learning for Domain-Invariant Training—0
All-neural beamformer for continuous speech separation—0
Correlation Distance Skip Connection Denoising Autoencoder (CDSK-DAE) for Speech Feature Enhancement—0
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition—0
Correlated Bigram LSA for Unsupervised Language Model Adaptation—0
Data Augmentation for End-to-end Code-switching Speech Recognition—0
Data Augmentation for End-to-End Speech Translation: FBK@IWSLT ‘19—0
Data Augmentation for Low-Resource Quechua ASR Improvement—0
Data Augmentation for Training Dialog Models Robust to Speech Recognition Errors—0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios—0
Attention Enhanced Citrinet for Speech Recognition—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition—0
Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition—0
Data Efficient Direct Speech-to-Text Translation with Modality Agnostic Meta-Learning—0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model—0
Correction Focused Language Model Training for Speech Recognition—0
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