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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 2426–2450 of 3012 papers

TitleStatusHype
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation—0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish—0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication—0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus—0
Zero-resource Speech Translation and Recognition with LLMs—0
Zero-Shot Automatic Pronunciation Assessment—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—0
Zero-shot Disfluency Detection for Indian Languages—0
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens—0
Zero-shot Speech Translation—0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora—0
Zipformer: A faster and better encoder for automatic speech recognition—0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities—0
100,000 Podcasts: A Spoken English Document Corpus—0
ZJU’s IWSLT 2021 Speech Translation System—0
Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech—0
Listening while Speaking and Visualizing: Improving ASR through Multimodal Chain—0
Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains—0
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition—0
Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR—0
Transformer-based Cascaded Multimodal Speech Translation—0
Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation—0
1SPU: 1-step Speech Processing Unit—0
Towards interfacing large language models with ASR systems using confidence measures and prompting—0
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic 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