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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
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts—0
Robust Neural Machine Translation with ASR Errors—0
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding—0
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems—0
Robust Prediction of Punctuation and Truecasing for Medical ASR—0
ROBUST SPEECH COMMAND RECOGNITION USING LABEL-DRIVEN TIME-FREQUENCY MASKING—0
Robust Spoken Language Understanding with RL-based Value Error Recovery—0
Robust Translation of French Live Speech Transcripts—0
Role of Intonation in Scoring Spoken English—0
Role of non-linear data processing on speech recognition task in the framework of reservoir computing—0
Role-specific Language Models for Processing Recorded Neuropsychological Exams—0
Romanization Encoding For Multilingual ASR—0
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition—0
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition—0
Run-and-back stitch search: novel block synchronous decoding for streaming encoder-decoder ASR—0
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus—0
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation—0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
Samrómur Children: An Icelandic Speech Corpus—0
Samr\'omur: Crowd-sourcing Data Collection for Icelandic Speech Recognition—0
SAN: a robust end-to-end ASR model architecture—0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation—0
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme—0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data—0
Scalable Multi Corpora Neural Language Models for ASR—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Scaling ASR Improves Zero and Few Shot Learning—0
Scaling Up Deliberation for Multilingual ASR—0
Scene-aware Far-field Automatic Speech Recognition—0
SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge—0
Security and Privacy Problems in Voice Assistant Applications: A Survey—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—0
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition—0
Self-critical Sequence Training for Automatic Speech Recognition—0
Self-Normalized Importance Sampling for Neural Language Modeling—0
Self-regularised Minimum Latency Training for Streaming Transformer-based Speech Recognition—0
Self-Supervised Learning-Based Source Separation for Meeting Data—0
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations—0
Self-supervised reinforcement learning for speaker localisation with the iCub humanoid robot—0
Self-supervised representations in speech-based depression detection—0
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text—0
Self-Supervised Speech Representation Learning: A Review—0
SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions—0
Semantic Data Augmentation for End-to-End Mandarin Speech Recognition—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