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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 2451–2500 of 3012 papers

TitleStatusHype
Kite: Automatic speech recognition for unmanned aerial vehicles—0
Scalable Multi Corpora Neural Language Models for ASR—0
Latent Dirichlet Allocation Based Acoustic Data Selection for Automatic Speech Recognition—0
Automated Cross-language Intelligibility Analysis of Parkinson's Disease Patients Using Speech Recognition Technologies—0
Comparison of Lattice-Free and Lattice-Based Sequence Discriminative Training Criteria for LVCSR—0
Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions—0
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers—0
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition—0
Multi-Span Acoustic Modelling using Raw Waveform Signals—0
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models—0
Code-Switching Detection Using ASR-Generated Language Posteriors—0
Multi-Graph Decoding for Code-Switching ASR—0
Multi-Stream End-to-End Speech Recognition—0
Advancing Speech Recognition With No Speech Or With Noisy Speech—0
Real to H-space Encoder for Speech Recognition—0
Adversarial Training for Multilingual Acoustic Modeling—0
Cumulative Adaptation for BLSTM Acoustic Models—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Lattice Transformer for Speech Translation—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
SpatialNet: A Declarative Resource for Spatial Relations—0
A user study to compare two conversational assistants designed for people with hearing impairments—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
Audio De-identification - a New Entity Recognition Task—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
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech—0
Effective Sentence Scoring Method using Bidirectional Language Model for Speech Recognition—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
Universal Adversarial Perturbations for Speech Recognition Systems—0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech—0
RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data AugmentationCode1
A Hardware-Oriented and Memory-Efficient Method for CTC Decoding—0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
Deep Learning for Audio Signal ProcessingCode0
English Broadcast News Speech Recognition by Humans and Machines—0
Adversarial Speaker Adaptation—0
Frequency Domain Multi-channel Acoustic Modeling for Distant Speech Recognition—0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition—0
Attentive Adversarial Learning for Domain-Invariant Training—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
SpecAugment: A Simple Data Augmentation Method for Automatic Speech RecognitionCode1
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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