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Automatic Speech Recognition

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Showing 1551–1600 of 3174 papers

TitleStatusHype
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
MeetDot: Videoconferencing with Live Translation Captions—0
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems—0
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—0
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition—0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax—0
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition—0
Mesures linguistiques automatiques pour l’évaluation des systèmes de Reconnaissance Automatique de la Parole (Automated linguistic measures for automatic speech recognition systems’ evaluation)—0
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
Meta Learning for End-to-End Low-Resource Speech Recognition—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge—0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation—0
Minimally Supervised Written-to-Spoken Text Normalization—0
Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition—0
Mitigating Noisy Inputs for Question Answering—0
MIXPGD: Hybrid Adversarial Training for Speech Recognition Systems—0
MixSpeech: Data Augmentation for Low-resource Automatic Speech Recognition—0
Mixture Encoder for Joint Speech Separation and Recognition—0
Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription—0
Mixture-of-Expert Conformer for Streaming Multilingual ASR—0
Mixture of LoRA Experts for Low-Resourced Multi-Accent Automatic Speech Recognition—0
Mixtures of Deep Neural Experts for Automated Speech Scoring—0
MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition—0
ML-LMCL: Mutual Learning and Large-Margin Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding—0
MLP-ASR: Sequence-length agnostic all-MLP architectures for speech recognition—0
MLP-based architecture with variable length input for automatic speech recognition—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
ML-SUPERB: Multilingual Speech Universal PERformance Benchmark—0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition—0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition—0
MobileASR: A resource-aware on-device learning framework for user voice personalization applications on mobile phones—0
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features—0
Model Adaptation for ASR in low-resource Indian Languages—0
Model-Based Approach for Measuring the Fairness in ASR—0
Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues—0
Modeling Confidence in Sequence-to-Sequence Models—0
Modeling Dependent Structure for Utterances in ASR Evaluation—0
Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models—0
Modelling prosodic structure using Artificial Neural Networks—0
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model—0
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition—0
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models—0
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries—0
Monolingual Recognizers Fusion for Code-switching Speech Recognition—0
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