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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 1151–1175 of 3012 papers

TitleStatusHype
Preliminary Study on SSCF-derived Polar Coordinate for ASR—0
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
Better Transcription of UK Supreme Court Hearings—0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Bidirectional Representations for Low Resource Spoken Language Understanding—0
Whose Emotion Matters? Speaking Activity Localisation without Prior KnowledgeCode0
Device Directedness with Contextual Cues for Spoken Dialog Systems—0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition—0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer—0
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review—0
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition—0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
Introducing Semantics into Speech Encoders—0
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation—0
Handling Trade-Offs in Speech Separation with Sparsely-Gated Mixture of Experts—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
Adaptive Multi-Corpora Language Model Training for 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