SOTAVerified

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

Showing 12761300 of 3012 papers

TitleStatusHype
From Weak Labels to Strong Results: Utilizing 5,000 Hours of Noisy Classroom Transcripts with Minimal Accurate Data0
FT Speech: Danish Parliament Speech Corpus0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model0
A Wav2vec2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition0
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning0
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices0
Arabic Language WEKA-Based Dialect Classifier for Arabic Automatic Speech Recognition Transcripts0
Fast and Accurate OOV Decoder on High-Level Features0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition0
Fast and Accurate Capitalization and Punctuation for Automatic Speech Recognition Using Transformer and Chunk Merging0
FusionFormer: Fusing Operations in Transformer for Efficient Streaming Speech Recognition0
Falling silent, lost for words ... Tracing personal involvement in interviews with Dutch war veterans0
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR0
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition0
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR0
Calibration of Phone Likelihoods in Automatic Speech Recognition0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems0
Gender and Dialect Bias in YouTube's Automatic Captions0
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior0
Generating Human Readable Transcript for Automatic Speech Recognition with Pre-trained Language Model0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems0
A Conformer-based Waveform-domain Neural Acoustic Echo Canceller Optimized for ASR Accuracy0
AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1Unverified
2TM-seq2seqTest WER9.7Unverified
3CTC/attentionTest WER8.2Unverified
4LF-MMI TDNNTest WER6.7Unverified
5Whisper-LLaMATest WER6.6Unverified
6End2end ConformerTest WER3.9Unverified
7End2end ConformerTest WER3.7Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7Unverified
9CTC/AttentionTest WER1.5Unverified
10WhisperTest WER1.3Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5Unverified
2CleanMel-L-maskCER14.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32Unverified
2Whisper-largev3-finetunedTest WER10.82Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98Unverified