SOTAVerified

Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

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Showing 4201–4250 of 6433 papers

TitleStatusHype
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition—0
Listening Comprehension over Argumentative Content—0
Listening to Multi-talker Conversations: Modular and End-to-end Perspectives—0
Listening while Speaking: Speech Chain by Deep Learning—0
Listening With Your Eyes: Towards a Practical Visual Speech Recognition System Using Deep Boltzmann Machines—0
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End—0
LiSTra, Automatic Speech Translation: English to Lingala case study—0
LiSTra Automatic Speech Translation: English to Lingala Case Study—0
LiteG2P: A fast, light and high accuracy model for grapheme-to-phoneme conversion—0
Literary and Colloquial Dialect Identification for Tamil using Acoustic Features—0
Literary and Colloquial Tamil Dialect Identification—0
LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data—0
LiveCC: Learning Video LLM with Streaming Speech Transcription at Scale—0
LLM-based phoneme-to-grapheme for phoneme-based speech recognition—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—0
LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenization—0
Load-balanced Gather-scatter Patterns for Sparse Deep Neural Networks—0
Local Feature or Mel Frequency Cepstral Coefficients - Which One is Better for MLN-Based Bangla Speech Recognition?—0
Locality enhanced dynamic biasing and sampling strategies for contextual ASR—0
Local Monotonic Attention Mechanism for End-to-End Speech and Language Processing—0
LoCoML: A Framework for Real-World ML Inference Pipelines—0
Lombard Effect for Bilingual Speakers in Cantonese and English: importance of spectro-temporal features—0
Long-distance Detection of Bioacoustic Events with Per-channel Energy Normalization—0
Longer is (Not Necessarily) Stronger: Punctuated Long-Sequence Training for Enhanced Speech Recognition and Translation—0
LongFNT: Long-form Speech Recognition with Factorized Neural Transducer—0
Long-form Simultaneous Speech Translation: Thesis Proposal—0
Incorporating VAD into ASR System by Multi-task Learning—0
Long Short-Term Memory based Convolutional Recurrent Neural Networks for Large Vocabulary Speech Recognition—0
Long Short-Term Memory Over Tree Structures—0
Long-span language modeling for speech recognition—0
Lookahead When It Matters: Adaptive Non-causal Transformers for Streaming Neural Transducers—0
Looking Enhances Listening: Recovering Missing Speech Using Images—0
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition—0
Loquacious Set: 25,000 Hours of Transcribed and Diverse English Speech Recognition Data for Research and Commercial Use—0
LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR—0
Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent—0
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition—0
Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models—0
Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech—0
Loudspeaker Beamforming to Enhance Speech Recognition Performance of Voice Driven Applications—0
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition—0
Low Latency ASR for Simultaneous Speech Translation—0
Low-Latency Speaker-Independent Continuous Speech Separation—0
Low-Level Physiological Implications of End-to-End Learning of Speech Recognition—0
Low-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition—0
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation—0
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models—0
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network—0
Low-resource Accent Classification in Geographically-proximate Settings: A Forensic and Sociophonetics Perspective—0
Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified