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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 1151–1200 of 3012 papers

TitleStatusHype
EURO: ESPnet Unsupervised ASR Open-source Toolkit—0
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation—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
Better Transcription of UK Supreme Court Hearings—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
Device Directedness with Contextual Cues for Spoken Dialog Systems—0
Whose Emotion Matters? Speaking Activity Localisation without Prior KnowledgeCode0
Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation—0
Benchmarking Evaluation Metrics for Code-Switching Automatic Speech Recognition—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
Unsupervised Model-based speaker adaptation of end-to-end lattice-free MMI model for speech recognition—0
Hey ASR System! Why Aren't You More Inclusive? Automatic Speech Recognition Systems' Bias and Proposed Bias Mitigation Techniques. A Literature Review—0
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer—0
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer—0
On using the UA-Speech and TORGO databases to validate automatic dysarthric speech classification approaches—0
Introducing Semantics into Speech Encoders—0
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
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
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
Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition—0
Robust Unstructured Knowledge Access in Conversational Dialogue with ASR ErrorsCode0
Streaming, fast and accurate on-device Inverse Text Normalization for Automatic Speech Recognition—0
End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics—0
Bridging Speech and Textual Pre-trained Models with Unsupervised ASR—0
LAMASSU: Streaming Language-Agnostic Multilingual Speech Recognition and Translation Using Neural Transducers—0
Evaluation of Automated Speech Recognition Systems for Conversational Speech: A Linguistic Perspective—0
Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion—0
Biased Self-supervised learning for ASR—0
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech—0
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system—0
Streaming Audio-Visual Speech Recognition with Alignment Regularization—0
Leveraging Domain Features for Detecting Adversarial Attacks Against Deep Speech Recognition in Noise—0
Probing Statistical Representations For End-To-End ASR—0
H_eval: A new hybrid evaluation metric for automatic speech recognition tasks—0
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss—0
More Speaking or More Speakers?—0
Monolingual Recognizers Fusion for Code-switching Speech Recognition—0
Towards Zero-Shot Code-Switched Speech Recognition—0
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder—0
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems—0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings—0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings—0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain—0
A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students—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