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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 776–800 of 3012 papers

TitleStatusHype
Can Contextual Biasing Remain Effective with Whisper and GPT-2?Code1
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts—0
Towards hate speech detection in low-resource languages: Comparing ASR to acoustic word embeddings on Wolof and Swahili—0
Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication—0
SlothSpeech: Denial-of-service Attack Against Speech Recognition ModelsCode0
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home—0
AfriNames: Most ASR models "butcher" African Names—0
Simple yet Effective Code-Switching Language Identification with Multitask Pre-Training and Transfer Learning—0
Accurate and Structured Pruning for Efficient Automatic Speech Recognition—0
VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition—0
Zero-Shot Automatic Pronunciation Assessment—0
Towards Selection of Text-to-speech Data to Augment ASR Training—0
Adapting Multi-Lingual ASR Models for Handling Multiple Talkers—0
STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions—0
Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer GeneratorCode0
Building Accurate Low Latency ASR for Streaming Voice Search—0
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice—0
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction—0
2-bit Conformer quantization for automatic speech recognition—0
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distributionCode0
INTapt: Information-Theoretic Adversarial Prompt Tuning for Enhanced Non-Native Speech Recognition—0
Improving Scheduled Sampling for Neural Transducer-based ASR—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