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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 1576–1600 of 3012 papers

TitleStatusHype
Wav2vec-Switch: Contrastive Learning from Original-noisy Speech Pairs for Robust Speech Recognition—0
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and SyllablesCode1
Advancing Momentum Pseudo-Labeling with Conformer and Initialization Strategy—0
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation—0
Interactive Feature Fusion for End-to-End Noise-Robust Speech RecognitionCode1
Evaluating User Perception of Speech Recognition System Quality with Semantic Distance Metric—0
Towards High-fidelity Singing Voice Conversion with Acoustic Reference and Contrastive Predictive Coding—0
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation—0
An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition—0
Wav2vec-S: Semi-Supervised Pre-Training for Low-Resource ASR—0
Personalized Automatic Speech Recognition Trained on Small Disordered Speech Datasets—0
Data Augmentation with Locally-time Reversed Speech for Automatic Speech Recognition—0
A Genetic Programming Approach To Zero-Shot Neural Architecture Ranking—0
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training—0
Input Length Matters: Improving RNN-T and MWER Training for Long-form Telephony Speech Recognition—0
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout—0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition—0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0—0
Knowledge Distillation for Neural Transducers from Large Self-Supervised Pre-trained Models—0
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR—0
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings—0
FAST-RIR: Fast neural diffuse room impulse response generatorCode1
Internal Language Model Adaptation with Text-Only Data for End-to-End 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