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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 1601–1650 of 3012 papers

TitleStatusHype
Multitask Learning for Adaptive Quality Estimation of Automatically Transcribed Utterances—0
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition—0
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention—0
Multi-task Learning with Cross Attention for Keyword Spotting—0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding—0
Multi-Task Self-Supervised Pre-Training for Music Classification—0
Multi-Temporal Lip-Audio Memory for Visual Speech Recognition—0
Multi-turn RNN-T for streaming recognition of multi-party speech—0
Multi-user VoiceFilter-Lite via Attentive Speaker Embedding—0
Multi-view Attention-based Speech Enhancement Model for Noise-robust Automatic Speech Recognition—0
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition—0
MuST-C: a Multilingual Speech Translation Corpus—0
Mutually-Constrained Monotonic Multihead Attention for Online ASR—0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition—0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances—0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space—0
NeMo: a toolkit for building AI applications using Neural Modules—0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training—0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks—0
Neural Kalman Filtering for Speech Enhancement—0
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion—0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration—0
Neural Morphological Analysis: Encoding-Decoding Canonical Segments—0
Neural Network Architectures for Arabic Dialect Identification—0
Neural Network-Based Modeling of Phonetic Durations—0
Neural Network Language Modeling with Letter-based Features and Importance Sampling—0
Neural Speech Translation at AppTek—0
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation—0
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System—0
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models—0
New Baseline in Automatic Speech Recognition for Northern S\'ami—0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision—0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding—0
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition—0
Noise Masking Attacks and Defenses for Pretrained Speech Models—0
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge—0
Noisy Training Improves E2E ASR for the Edge—0
Non-Autoregressive Chinese ASR Error Correction with Phonological Training—0
Non-autoregressive End-to-end Approaches for Joint Automatic Speech Recognition and Spoken Language Understanding—0
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input—0
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition—0
Non-autoregressive Transformer-based End-to-end ASR using BERT—0
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition—0
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures—0
Non-Parallel Voice Conversion for ASR Augmentation—0
Nonparametric Bayesian Double Articulation Analyzer for Direct Language Acquisition from Continuous Speech Signals—0
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—0
No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation—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