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Normal-Anchored First-Order Model-Agnostic Meta-Learning based Whisper Fine-Tuning for Enhancing Fairness of Cleft Lip and Palate Speech Recognition
语音识别 | 5.9/10
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SoniSpeech: A Large-Scale Open-Vocabulary Tri-Modal Dataset for Wearable Silent Speech Interfaces
语音识别 | 8.3/10
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Leveraging Beam Search Information for Confidence Estimation in E2E ASR
语音识别 | 6.3/10
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ParaASR: Multi-Token Prediction for Fast and Long-Context LLM-Based Speech Recognition
语音识别 | 6.7/10
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AgenticASR: Refining Speech Recognition in Real-World Scenarios via an Agentic Approach
语音识别 | 6.8/10
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Does EEG Foundation Models Transfer to Speech? A Benchmark on Overt and Imagined Speech Decoding
语音识别 | 6.5/10
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Enhancing Law-Enforcement Audio Transcription: A LoRA-Based Adaptation of Whisper for BWC Footage
语音识别 | 4.8/10
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Dissecting Sensitivity to Training Language in Self-Supervised Speech Learning Using Neural Audio Codec Tokens
语音识别 | 6.3/10
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Voice Memory for Agentic Speech Recognition
语音识别 | 8.2/10
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Evaluation of forced alignment of code-mixed speech: the case of Hindi-English
语音识别 | 5.4/10
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SpeechLLM Meets Federated Learning for End-to-End ASR: English and Italian Case Studies
语音识别 | 7.1/10
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Towards Operational Conversational Intelligence: A Speech Intelligence Framework
说话人日志 | 6.4/10
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Earnings25: A Comprehensive 500-Hour Speech Benchmark for Finance
语音识别 | 7.1/10
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Indic DiarBench: A Multilingual Joint Diarization and ASR Benchmark for Indian Languages
说话人日志 | 7.1/10
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MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition
语音识别 | 6.7/10
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Listen, Do Not Copy: Internalizing Audio-Grounded Scaffold Context for Robust Omni-Model Speech Understanding
语音识别 | 6.6/10
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MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond
语音识别 | 8.2/10
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DONDO: Open w2v-BERT Speech-Recognition Base Models for African Languages
语音识别 | 8.1/10
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From Read Speech to Spoken Digits: A Task-Specific Evaluation of Speech Privacy With Informed Attackers
语音识别 | 7.5/10
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Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin
语音识别 | 6.2/10