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 1551–1600 of 6433 papers

TitleStatusHype
Cross-Attention End-to-End ASR for Two-Party Conversations—0
Cross-Attention Fusion of Visual and Geometric Features for Large Vocabulary Arabic Lipreading—0
Cross-Attribute Matrix Factorization Model with Shared User Embedding—0
Cross-document Event Coreference Resolution based on Cross-media Features—0
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR—0
A Treatise On FST Lattice Based MMI Training—0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents—0
Cross-Lingual Conversational Speech Summarization with Large Language Models—0
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition—0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers—0
Cross-Lingual Language Modeling with Syntactic Reordering for Low-Resource Speech Recognition—0
Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis—0
Cross-lingual projection for class-based language models—0
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition—0
CNN-based MultiChannel End-to-End Speech Recognition for everyday home environments—0
Cross-lingual Synonymy Overlap—0
CNN architecture extraction on edge GPU—0
Cross-Lingual Transfer Learning for Speech Translation—0
Cross-Lingual Word Embeddings for Low-Resource Language Modeling—0
Cross-modal Alignment with Optimal Transport for CTC-based ASR—0
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection—0
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing—0
CMU’s IWSLT 2022 Dialect Speech Translation System—0
Cross-Modal Mutual Learning for Cued Speech Recognition—0
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition—0
Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition—0
A Review of Deep Learning Techniques for Speech Processing—0
Cross-Utterance Language Models with Acoustic Error Sampling—0
A Dynamic Programming Algorithm for Computing N-gram Posteriors from Lattices—0
Community Detection Clustering via Gumbel Softmax—0
CTC Alignments Improve Autoregressive Translation—0
CTC-Assisted LLM-Based Contextual ASR—0
CTC Blank Triggered Dynamic Layer-Skipping for Efficient CTC-based Speech Recognition—0
CTC-DRO: Robust Optimization for Reducing Language Disparities in Speech Recognition—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
CTC Variations Through New WFST Topologies—0
Cumulative Adaptation for BLSTM Acoustic Models—0
CUNI Neural ASR with Phoneme-Level Intermediate Step for -Native at IWSLT 2020—0
Current Challenges in Spoken Dialogue Systems and Why They Are Critical for Those Living with Dementia—0
Curriculum optimization for low-resource speech recognition—0
Curriculum Pre-training for End-to-End Speech Translation—0
Curvature: A signature for Action Recognition in Video Sequences—0
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding—0
Customizing Grapheme-to-Phoneme System for Non-Trivial Transcription Problems in Bangla Language—0
Customizing Speech Recognition Model with Large Language Model Feedback—0
Cycle-consistency training for end-to-end speech recognition—0
Cycle-Consistent GAN Front-End to Improve ASR Robustness to Perturbed Speech—0
Deep Bayesian Natural Language Processing—0
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition—0
Deep Learning for Forecasting Stock Returns in the Cross-Section—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