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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 2776–2800 of 3012 papers

TitleStatusHype
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Towards Quantum Language Models—0
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video—0
Information Theoretic Analysis of DNN-HMM Acoustic Modeling—0
An Improved Residual LSTM Architecture for Acoustic Modeling—0
Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts—0
Language Identification Using Deep Convolutional Recurrent Neural NetworksCode0
Massively Multilingual Neural Grapheme-to-Phoneme ConversionCode0
Utterance Intent Classification of a Spoken Dialogue System with Efficiently Untied Recursive Autoencoders—0
Attentive listening system with backchanneling, response generation and flexible turn-taking—0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition—0
Fast and Accurate OOV Decoder on High-Level Features—0
Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training—0
Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation—0
Encoding Word Confusion Networks with Recurrent Neural Networks for Dialog State Tracking—0
Listening while Speaking: Speech Chain by Deep Learning—0
Predicting Causes of Reformulation in Intelligent Assistants—0
Automatic Speech Recognition with Very Large Conversational Finnish and Estonian Vocabularies—0
Unsupervised Submodular Rank Aggregation on Score-based PermutationsCode0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
Joint CTC/attention decoding for end-to-end speech recognition—0
Automatic Quality Estimation for ASR System Combination—0
Modelling prosodic structure using Artificial Neural Networks—0
End-to-end neural networks for subvocal 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