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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 951–975 of 3012 papers

TitleStatusHype
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithmCode1
End-to-End Speech Translation of Arabic to English Broadcast News—0
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition—0
Improved Speech Pre-Training with Supervision-Enhanced Acoustic Unit—0
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information—0
Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers—0
SoftCTC -- Semi-Supervised Learning for Text Recognition using Soft Pseudo-LabelsCode1
Unsupervised Fine-Tuning Data Selection for ASR Using Self-Supervised Speech Models—0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers—0
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition—0
Preliminary Study on SSCF-derived Polar Coordinate for ASR—0
EURO: ESPnet Unsupervised ASR Open-source Toolkit—0
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition—0
Evaluating and reducing the distance between synthetic and real speech distributions—0
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation—0
Better Transcription of UK Supreme Court Hearings—0
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition—0
Bidirectional Representations for Low Resource Spoken Language Understanding—0
Multitask Learning 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
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition—0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
SSCFormer: Push the Limit of Chunk-wise Conformer for Streaming ASR Using Sequentially Sampled Chunks and Chunked Causal Convolution—0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale—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