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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 2526–2550 of 3012 papers

TitleStatusHype
All-neural online source separation, counting, and diarization for meeting analysis—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—0
Enhanced Robot Speech Recognition Using Biomimetic Binaural Sound Source Localization—0
Using multi-task learning to improve the performance of acoustic-to-word and conventional hybrid models—0
Weighted-Sampling Audio Adversarial Example Attack—0
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools—0
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning—0
Speaker Adaptation for End-to-End CTC Models—0
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition—0
Pansori: ASR Corpus Generation from Open Online Video ContentsCode0
Streaming Voice Query Recognition using Causal Convolutional Recurrent Neural Networks—0
Multiple topic identification in human/human conversations—0
The Recognition Of Persian Phonemes Using PPNet—0
E-RNN: Design Optimization for Efficient Recurrent Neural Networks in FPGAs—0
End-to-end contextual speech recognition using class language models and a token passing decoder—0
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices—0
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR—0
On the Inductive Bias of Word-Character-Level Multi-Task Learning for Speech Recognition—0
Speech recognition with quaternion neural networks—0
WEST: Word Encoded Sequence Transducers—0
Investigating the Effects of Word Substitution Errors on Sentence EmbeddingsCode0
An Online Attention-based Model for Speech Recognition—0
Exploring RNN-Transducer for Chinese Speech Recognition—0
Corpus Phonetics Tutorial—0
Multi-encoder multi-resolution framework for end-to-end 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