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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 2651–2675 of 3012 papers

TitleStatusHype
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models—0
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities—0
Automatic Detection of Code-switching Style from Acoustics—0
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages—0
Word Error Rate Estimation for Speech Recognition: e-WERCode1
Global-Locally Self-Attentive Encoder for Dialogue State Tracking—0
Language Modeling for Code-Mixing: The Role of Linguistic Theory based Synthetic Data—0
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR—0
Contextual Language Model Adaptation for Conversational Agents—0
Towards Automated Single Channel Source Separation using Neural Networks—0
Quaternion Convolutional Neural Networks for End-to-End Automatic Speech RecognitionCode0
Recurrent DNNs and its Ensembles on the TIMIT Phone Recognition TaskCode0
Speaker Adapted Beamforming for Multi-Channel Automatic Speech Recognition—0
Extending Recurrent Neural Aligner for Streaming End-to-End Speech Recognition in Mandarin—0
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition—0
Quaternion Recurrent Neural NetworksCode0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages—0
LSTM Benchmarks for Deep Learning FrameworksCode0
Binarized LSTM Language Model—0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems—0
Role-specific Language Models for Processing Recorded Neuropsychological Exams—0
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio—0
End-to-end named entity extraction from speech—0
Multimodal Speaker Segmentation and Diarization using Lexical and Acoustic Cues via Sequence to Sequence Neural Networks—0
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfacesCode1
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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