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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 2726–2750 of 3012 papers

TitleStatusHype
Evaluating Automatic Speech Recognition in Translation—0
Challenges in Speech Recognition and Translation of High-Value Low-Density Polysynthetic Languages—0
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models—0
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages—0
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation EvaluationCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition—0
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription—0
A Corpus for Modeling Word Importance in Spoken Dialogue Transcripts—0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition—0
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model—0
Audio Adversarial Examples: Targeted Attacks on Speech-to-TextCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
Learning Robust Dialog Policies in Noisy Environments—0
Building competitive direct acoustics-to-word models for English conversational speech recognition—0
An analysis of incorporating an external language model into a sequence-to-sequence model—0
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence ModelsCode1
State-of-the-art Speech Recognition With Sequence-to-Sequence ModelsCode1
Exploiting Nontrivial Connectivity for Automatic Speech Recognition—0
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition—0
Speech recognition for medical conversations—0
E-PUR: An Energy-Efficient Processing Unit for Recurrent Neural Networks—0
Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition—0
Phonemic and Graphemic Multilingual CTC Based Speech Recognition—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