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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2476–2500 of 3012 papers

TitleStatusHype
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models—0
Multi-Span Acoustic Modelling using Raw Waveform Signals—0
Code-Switching Detection Using ASR-Generated Language Posteriors—0
Multi-Graph Decoding for Code-Switching ASR—0
Adversarial Training for Multilingual Acoustic Modeling—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
Multi-Stream End-to-End Speech Recognition—0
Real to H-space Encoder for Speech Recognition—0
Cumulative Adaptation for BLSTM Acoustic Models—0
Lattice Transformer for Speech Translation—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Audio De-identification - a New Entity Recognition Task—0
SpatialNet: A Declarative Resource for Spatial Relations—0
Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership—0
MuST-C: a Multilingual Speech Translation Corpus—0
A user study to compare two conversational assistants designed for people with hearing impairments—0
Building and Evaluation of a Real Room Impulse Response Dataset—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASRCode0
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition—0
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech—0
Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models—0
Almost Unsupervised Text to Speech and Automatic Speech Recognition—0
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification—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