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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 1151–1175 of 3012 papers

TitleStatusHype
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-Task Language Modeling—0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments—0
A Speech Test Set of Practice Business Presentations with Additional Relevant Texts—0
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR—0
Cloud-based Automatic Speech Recognition Systems for Southeast Asian Languages—0
Ask2Mask: Guided Data Selection for Masked Speech Modeling—0
AfriNames: Most ASR models "butcher" African Names—0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition—0
Clinical Dialogue Transcription Error Correction using Seq2Seq Models—0
Clinical BERTScore: An Improved Measure of Automatic Speech Recognition Performance in Clinical Settings—0
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers—0
A Simple Baseline for Domain Adaptation in End to End ASR Systems Using Synthetic Data—0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines—0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition—0
Chinese Medical Speech Recognition with Punctuated Hypothesis—0
A Semi-Automated Live Interlingual Communication Workflow Featuring Intralingual Respeaking: Evaluation and Benchmarking—0
A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding—0
A Corpus of Read and Spontaneous Upper Saxon German Speech for ASR Evaluation—0
Chinese-LiPS: A Chinese audio-visual speech recognition dataset with Lip-reading and Presentation Slides—0
A Semantic Analyzer for the Comprehension of the Spontaneous Arabic Speech—0
Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement—0
Characterizing Audio Adversarial Examples Using Temporal Dependency—0
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding—0
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech—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