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Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

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Showing 1551–1600 of 3012 papers

TitleStatusHype
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme—0
Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings—0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass—0
A two-step approach to leverage contextual data: speech recognition in air-traffic communications—0
Polyphonic pitch detection with convolutional recurrent neural networks—0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The RoyalFlush System of Speech Recognition for M2MeT Challenge—0
Joint Speech Recognition and Audio Captioning—0
Error Correction in ASR using Sequence-to-Sequence Models—0
ASR-Aware End-to-end Neural Diarization—0
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT—0
Language Dependencies in Adversarial Attacks on Speech Recognition Systems—0
Visualizing Automatic Speech Recognition -- Means for a Better Understanding?—0
BEA-Base: A Benchmark for ASR of Spontaneous Hungarian—0
Star Temporal Classification: Sequence Classification with Partially Labeled DataCode0
Reducing language context confusion for end-to-end code-switching automatic speech recognition—0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Synthesizing Dysarthric Speech Using Multi-talker TTS for Dysarthric Speech Recognition—0
Discovering Phonetic Inventories with Crosslingual Automatic Speech RecognitionCode0
On the Effectiveness of Pinyin-Character Dual-Decoding for End-to-End Mandarin Chinese ASR—0
The Norwegian Parliamentary Speech Corpus—0
Run-and-back stitch search: novel block synchronous decoding for streaming encoder-decoder ASR—0
Transformer-Based Video Front-Ends for Audio-Visual Speech Recognition for Single and Multi-Person Video—0
Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models—0
Improving the fusion of acoustic and text representations in RNN-T—0
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition—0
Human and Automatic Speech Recognition Performance on German Oral History Interviews—0
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR—0
DUAL: Textless Spoken Question Answering with Speech Discrete Unit Adaptive Learning—0
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings—0
Recent Progress in the CUHK Dysarthric Speech Recognition System—0
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition—0
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection—0
A Likelihood Ratio based Domain Adaptation Method for E2E Models—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural NetworksCode0
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset—0
Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question—0
Tencent-MVSE: A Large-Scale Benchmark Dataset for Multi-Modal Video Similarity Evaluation—0
Multi-Dialect Arabic Speech Recognition—0
Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition—0
Voice Quality and Pitch Features in Transformer-Based Speech Recognition—0
Multi-turn RNN-T for streaming recognition of multi-party speech—0
Integrating Knowledge in End-to-End Automatic Speech Recognition for Mandarin-English Code-Switching—0
Continual Learning for Monolingual End-to-End Automatic Speech RecognitionCode0
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems—0
Real-Time Neural Voice Camouflage—0
Improving Hybrid CTC/Attention End-to-end Speech Recognition with Pretrained Acoustic and Language Model—0
Robustifying automatic speech recognition by extracting slowly varying features—0
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition—0
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1—Unverified
2TM-seq2seqTest WER9.7—Unverified
3CTC/attentionTest WER8.2—Unverified
4LF-MMI TDNNTest WER6.7—Unverified
5Whisper-LLaMATest WER6.6—Unverified
6End2end ConformerTest WER3.9—Unverified
7End2end ConformerTest WER3.7—Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7—Unverified
9CTC/AttentionTest WER1.5—Unverified
10WhisperTest WER1.3—Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5—Unverified
2CleanMel-L-maskCER14.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32—Unverified
2Whisper-largev3-finetunedTest WER10.82—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89—Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98—Unverified