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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 1701–1750 of 3012 papers

TitleStatusHype
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings—0
Enabling On-Device Training of Speech Recognition Models with Federated Dropout—0
Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition—0
Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers using End-to-End Speaker-Attributed ASR—0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0—0
Spell my name: keyword boosted speech recognition—0
CTC Variations Through New WFST Topologies—0
Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition—0
Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition—0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
ASR Rescoring and Confidence Estimation with ELECTRA—0
Is Attention always needed? A Case Study on Language Identification from Speech—0
Building a Noisy Audio Dataset to Evaluate Machine Learning Approaches for Automatic Speech Recognition Systems—0
Towards efficient end-to-end speech recognition with biologically-inspired neural networks—0
Exploiting Pre-Trained ASR Models for Alzheimer's Disease Recognition Through Spontaneous Speech—0
Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning—0
Employing low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement—0
Exploring the Integration of E2E ASR and Pronunciation Modeling for English Mispronunciation Detection—0
Chinese Medical Speech Recognition with Punctuated Hypothesis—0
Improving Punctuation Restoration for Speech Transcripts via External Data—0
SpliceOut: A Simple and Efficient Audio Augmentation Method—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
Understanding the Role of Self Attention for Efficient Speech Recognition—0
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards—0
Synthesising Audio Adversarial Examples for Automatic Speech Recognition—0
PhaseFool: Phase-oriented Audio Adversarial Examples via Energy Dissipation—0
MLP-based architecture with variable length input for automatic speech recognition—0
Demystifying Limited Adversarial Transferability in Automatic Speech Recognition Systems—0
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition—0
Conditioning Sequence-to-sequence Networks with Learned Activations—0
Private Language Model Adaptation for Speech Recognition—0
Word-level confidence estimation for RNN transducers—0
Challenges and Opportunities of Speech Recognition for Bengali Language—0
BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition—0
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts—0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Audio-Visual Speech Recognition is Worth 32328 Voxels—0
iRNN: Integer-only Recurrent Neural Network—0
MeetDot: Videoconferencing with Live Translation Captions—0
Model-Based Approach for Measuring the Fairness in ASR—0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding—0
Utterance-level neural confidence measure for end-to-end children speech recognition—0
PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription—0
Improving Accent Identification and Accented Speech Recognition Under a Framework of Self-supervised Learning—0
Improving Streaming Transformer Based ASR Under a Framework of Self-supervised Learning—0
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition—0
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech—0
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search DegradationCode0
Unsupervised Domain Adaptation Schemes for Building ASR in Low-resource Languages—0
Remember the context! ASR slot error correction through memorization—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