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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 2101–2150 of 3012 papers

TitleStatusHype
VenoMave: Targeted Poisoning Against Speech RecognitionCode0
Towards End-to-End Training of Automatic Speech Recognition for Nigerian PidginCode0
Cascaded Models With Cyclic Feedback For Direct Speech Translation—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
Knowledge Distillation for Improved Accuracy in Spoken Question Answering—0
FastEmit: Low-latency Streaming ASR with Sequence-level Emission RegularizationCode0
Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction—0
Knowledge Transfer for Efficient On-device False Trigger Mitigation—0
Ensemble Chinese End-to-End Spoken Language Understanding for Abnormal Event Detection from audio stream—0
Towards Data Distillation for End-to-end Spoken Conversational Question Answering—0
Studying the Similarity of COVID-19 Sounds based on Correlation Analysis of MFCC—0
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures—0
Multimodal Speech Recognition with Unstructured Audio Masking—0
Lightweight End-to-End Speech Recognition from Raw Audio Data Using Sinc-Convolutions—0
Exploiting Spectral Augmentation for Code-Switched Spoken Language Identification—0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling—0
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS—0
WER we are and WER we think we are—0
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTSCode0
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text CorpusCode0
Fine-Grained Grounding for Multimodal Speech RecognitionCode0
A Study on Lip Localization Techniques used for Lip reading from a Video—0
FluentNet: End-to-End Detection of Speech Disfluency with Deep Learning—0
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic HandsCode0
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition—0
Multi-modal embeddings using multi-task learning for emotion recognition—0
Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition—0
Robust Spoken Language Understanding with RL-based Value Error Recovery—0
Silent Speech Interfaces for Speech Restoration: A Review—0
Voice Conversion by Cascading Automatic Speech Recognition and Text-to-Speech Synthesis with Prosody Transfer—0
Convolutional Speech Recognition with Pitch and Voice Quality Features—0
Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition—0
Data augmentation using prosody and false starts to recognize non-native children's speechCode0
Learned Transferable Architectures Can Surpass Hand-Designed Architectures for Large Scale Speech Recognition—0
Aphasic Speech Recognition using a Mixture of Speech Intelligibility Experts—0
Improving Tail Performance of a Deliberation E2E ASR Model Using a Large Text Corpus—0
Cross-Utterance Language Models with Acoustic Error Sampling—0
Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical StudyCode0
Speech To Semantics: Improve ASR and NLU Jointly via All-Neural Interfaces—0
Large-scale Transfer Learning for Low-resource Spoken Language Understanding—0
MASRI-HEADSET: A Maltese Corpus for Speech Recognition—0
Conv-Transformer Transducer: Low Latency, Low Frame Rate, Streamable End-to-End Speech Recognition—0
Transfer Learning Approaches for Streaming End-to-End Speech Recognition System—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Transformer with Bidirectional Decoder for Speech Recognition—0
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition—0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition—0
Investigation of Speaker-adaptation methods in Transformer based ASR—0
Deep Learning Based Dereverberation of Temporal Envelopesfor Robust Speech Recognition—0
Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions—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