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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 2501–2550 of 3012 papers

TitleStatusHype
Universal Adversarial Perturbations for Speech Recognition Systems—0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech—0
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Deep Learning for Audio Signal ProcessingCode0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
English Broadcast News Speech Recognition by Humans and Machines—0
Adversarial Speaker Adaptation—0
Attentive Adversarial Learning for Domain-Invariant Training—0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition—0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition—0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
Realizing Petabyte Scale Acoustic ModelingCode0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances—0
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions—0
TTS Skins: Speaker Conversion via ASR—0
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition—0
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation—0
A Multi-Task Learning Framework for Overcoming the Catastrophic Forgetting in Automatic Speech Recognition—0
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge—0
Unsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech RecognitionCode0
Distributed Deep Learning Strategies For Automatic Speech Recognition—0
Performance Monitoring for End-to-End Speech Recognition—0
Exploring Methods for the Automatic Detection of Errors in Manual Transcription—0
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data—0
Spoken Language Intent Detection using Confusion2VecCode0
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion—0
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others—0
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition—0
Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues—0
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech RecognitionCode0
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition—0
Audio De-identification: A New Entity Recognition Task—0
Automatic assessment of spoken language proficiency of non-native children—0
Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream WeightsCode0
Singing voice conversion with non-parallel data—0
Bridging the Gap Between Monaural Speech Enhancement and Recognition with Distortion-Independent Acoustic Modeling—0
Speech Recognition with no speech or with noisy speech—0
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis—0
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions—0
All-neural online source separation, counting, and diarization for meeting analysis—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization—0
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools—0
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning—0
Speaker Adaptation for End-to-End CTC Models—0
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition—0
Pansori: ASR Corpus Generation from Open Online Video ContentsCode0
Streaming Voice Query Recognition using Causal Convolutional Recurrent Neural Networks—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