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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 1276–1300 of 3012 papers

TitleStatusHype
From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data—0
FT Speech: Danish Parliament Speech Corpus—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
An Investigation on Applying Acoustic Feature Conversion to ASR of Adult and Child Speech—0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices—0
Challenges of Computational Processing of Code-Switching—0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition—0
FusionFormer: Fusing Operations in Transformer for Efficient Streaming Speech Recognition—0
Fusion Models for Improved Visual Captioning—0
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR—0
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition—0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR—0
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition—0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems—0
Gender and Dialect Bias in YouTube's Automatic Captions—0
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance—0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior—0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model—0
Generating Robust Audio Adversarial Examples using Iterative Proportional Clipping—0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems—0
Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream—0
Enriching ASR Lattices with POS Tags for Dependency Parsing—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