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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 2201–2250 of 3012 papers

TitleStatusHype
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
Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection—0
NUVA: A Naming Utterance Verifier for Aphasia Treatment—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
OAVA: the open audio-visual archives aggregator—0
Off-topic Response Detection for Spontaneous Spoken English Assessment—0
OLR 2021 Challenge: Datasets, Rules and Baselines—0
Omni-sparsity DNN: Fast Sparsity Optimization for On-Device Streaming E2E ASR via Supernet—0
On Building Spoken Language Understanding Systems for Low Resourced Languages—0
On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Mode—0
On Construction of the ASR-oriented Indian English Pronunciation Dictionary—0
On-Device detection of sentence completion for voice assistants with low-memory footprint—0
On-Device Personalization of Automatic Speech Recognition Models for Disordered Speech—0
On-Device Speaker Anonymization of Acoustic Embeddings for ASR based onFlexible Location Gradient Reversal Layer—0
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement—0
One model to rule them all ? Towards End-to-End Joint Speaker Diarization and Speech Recognition—0
One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-off in Machine Learning Cloud Service APIs via Tolerance Tiers—0
One-To-Many Multilingual End-to-end Speech Translation—0
On Knowledge Distillation for Direct Speech Translation—0
On Knowledge Distillation for Translating Erroneous Speech Transcriptions—0
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models—0
Online Automatic Speech Recognition with Listen, Attend and Spell Model—0
Online Continual Learning of End-to-End Speech Recognition Models—0
Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture—0
On Modular Training of Neural Acoustics-to-Word Model for LVCSR—0
On Neural Phone Recognition of Mixed-Source ECoG Signals—0
On Prosody Modeling for ASR+TTS based Voice Conversion—0
On Sampling-Based Training Criteria for Neural Language Modeling—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