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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 2551–2600 of 3012 papers

TitleStatusHype
Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead—0
Snow Mountain: Dataset of Audio Recordings of The Bible in Low Resource Languages—0
SNRi Target Training for Joint Speech Enhancement and Recognition—0
SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition—0
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset—0
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
Space-Efficient Representation of Entity-centric Query Language Models—0
Sparsely Shared LoRA on Whisper for Child Speech Recognition—0
Sparse Non-negative Matrix Language Modeling—0
Sparse Transcription—0
Sparsification via Compressed Sensing for Automatic Speech Recognition—0
Spatial Audio Processing with Large Language Model on Wearable Devices—0
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments—0
SpatialNet: A Declarative Resource for Spatial Relations—0
Speaker Adaptation for Attention-Based End-to-End Speech Recognition—0
Speaker Adaptation for End-to-End CTC Models—0
Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition—0
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition—0
Speaker and Language Change Detection using Wav2vec2 and Whisper—0
Speaker Anonymization with Phonetic Intermediate Representations—0
Speaker-aware speech-transformer—0
Speaker Change Detection for Transformer Transducer ASR—0
Speaker conditioning of acoustic models using affine transformation for multi-speaker speech recognition—0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR—0
Speaker Diarization with Lexical Information—0
Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models—0
Speaker Reinforcement Using Target Source Extraction for Robust Automatic Speech Recognition—0
Speaker Selective Beamformer with Keyword Mask Estimation—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Speak & Improve Challenge 2025: Tasks and Baseline Systems—0
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback—0
SpecAugment on Large Scale Datasets—0
Spectral Modification Based Data Augmentation For Improving End-to-End ASR For Children's Speech—0
Spectral modification for recognition of children’s speech undermismatched conditions—0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speech—0
Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator—0
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks—0
Speech Aware Dialog System Technology Challenge (DSTC11)—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language—0
Speech Diarization and ASR with GMM—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks—0
Speech enhancement with frequency domain auto-regressive modeling—0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale—0
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition—0
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task—0
Speech Recognition by Simply Fine-tuning BERT—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