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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 1051–1075 of 3012 papers

TitleStatusHype
Context-Aware Transformer Transducer for Speech Recognition—0
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions—0
Context-aware Fine-tuning of Self-supervised Speech Models—0
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems—0
Ain't Misbehavin' -- Using LLMs to Generate Expressive Robot Behavior in Conversations with the Tabletop Robot Haru—0
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages—0
Accurate and Structured Pruning for Efficient Automatic Speech Recognition—0
Content-Aware Speaker Embeddings for Speaker Diarisation—0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings—0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification—0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems—0
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data—0
A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech—0
Consistency Based Unsupervised Self-training For ASR Personalisation—0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling—0
Connecting Speech Encoder and Large Language Model for ASR—0
Connecting Humanities and Social Sciences: Applying Language and Speech Technology to Online Panel Surveys—0
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers—0
Acoustic-Phonetic Approach for ASR of Less Resourced Languages Using Monolingual and Cross-Lingual Information—0
Confusion2Vec: Towards Enriching Vector Space Word Representations with Representational Ambiguities—0
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices—0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition—0
Conformer-based Hybrid ASR System for Switchboard Dataset—0
Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping—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