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Automatic Speech Recognition

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Showing 2101–2125 of 3174 papers

TitleStatusHype
Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation—0
Leveraging Weakly Supervised Data to Improve End-to-End Speech-to-Text Translation—0
LeVoice ASR Systems for the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge—0
Lexical Speaker Error Correction: Leveraging Language Models for Speaker Diarization Error Correction—0
Lexicon and Attention based Handwritten Text Recognition System—0
Lightweight and Robust Multi-Channel End-to-End Speech Recognition with Spherical Harmonic Transform—0
Lightweight End-to-End Speech Recognition from Raw Audio Data Using Sinc-Convolutions—0
Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems—0
Lightweight Target-Speaker-Based Overlap Transcription for Practical Streaming ASR—0
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition—0
LinTO Audio and Textual Datasets to Train and Evaluate Automatic Speech Recognition in Tunisian Arabic Dialect—0
LinTO Platform: A Smart Open Voice Assistant for Business Environments—0
LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models—0
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models—0
Listening while Speaking: Speech Chain by Deep Learning—0
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End—0
LiSTra, Automatic Speech Translation: English to Lingala case study—0
LiSTra Automatic Speech Translation: English to Lingala Case Study—0
Literary and Colloquial Dialect Identification for Tamil using Acoustic Features—0
LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data—0
LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—0
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?—0
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