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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 1351–1375 of 3012 papers

TitleStatusHype
Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation—0
Homophone-based Label Smoothing in End-to-End Automatic Speech Recognition—0
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models—0
Enhancing Unsupervised Speech Recognition with Diffusion GANs—0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders—0
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR—0
How does end-to-end speech recognition training impact speech enhancement artifacts?—0
Blending LSTMs into CNNs—0
How Might We Create Better Benchmarks for Speech Recognition?—0
Comparative Analysis of the wav2vec 2.0 Feature Extractor—0
How to Learn a New Language? An Efficient Solution for Self-Supervised Learning Models Unseen Languages Adaption in Low-Resource Scenario—0
How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets—0
How to Train Dependency Parsers with Inexact Search for Joint Sentence Boundary Detection and Parsing of Entire Documents—0
How transferable are features in convolutional neural network acoustic models across languages?—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
HTEC: Human Transcription Error Correction—0
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition—0
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning—0
Human and Automatic Speech Recognition Performance on German Oral History Interviews—0
Human-Informed Speakers and Interpreters Analysis in the WAW Corpus and an Automatic Method for Calculating Interpreters' D\'ecalage—0
Human Listening and Live Captioning: Multi-Task Training for Speech Enhancement—0
Comparing Two Basic Methods for Discriminating Between Similar Languages and Varieties—0
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru for Speech Recognition—0
Huqariq: A Multilingual Speech Corpus of Native Languages of Peru forSpeech Recognition—0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024—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