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 3501–3550 of 6433 papers

TitleStatusHype
OpenDial: A Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules—0
Open-Domain Name Error Detection using a Multi-Task RNN—0
Open Ended Intelligence: The individuation of Intelligent Agents—0
Open Implementation and Study of BEST-RQ for Speech Processing—0
OpenSeq2Seq: Extensible Toolkit for Distributed and Mixed Precision Training of Sequence-to-Sequence Models—0
Open Set Modulation Recognition Based on Dual-Channel LSTM Model—0
Open-Source Conversational AI with SpeechBrain 1.0—0
Open-Source High Quality Speech Datasets for Basque, Catalan and Galician—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
Open Terminology Management and Sharing Toolkit for Federation of Terminology Databases—0
Open-vocabulary Keyword-spotting with Adaptive Instance Normalization—0
Operational Assessment of Keyword Search on Oral History—0
Opportunities & Challenges In Automatic Speech Recognition—0
Optimal Data Set Selection: An Application to Grapheme-to-Phoneme Conversion—0
Optimal Transport-based Adaptation in Dysarthric Speech Tasks—0
Optimising AI Training Deployments using Graph Compilers and Containers—0
Optimising Incremental Dialogue Decisions Using Information Density for Interactive Systems—0
Optimization Methods in Deep Learning: A Comprehensive Overview—0
Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification—0
Optimized Tokenization for Transcribed Error Correction—0
Optimizing Bilingual Neural Transducer with Synthetic Code-switching Text Generation—0
Optimizing Byte-level Representation for End-to-end ASR—0
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval—0
Optimizing expected word error rate via sampling for speech recognition—0
Optimizing Generative Dialog State Tracker via Cascading Gradient Descent—0
Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment—0
Optimizing Segmentation Strategies for Simultaneous Speech Translation—0
Optimizing Speech Recognition For The Edge—0
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation—0
OpusLM: A Family of Open Unified Speech Language Models—0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification—0
OTF: Optimal Transport based Fusion of Supervised and Self-Supervised Learning Models for Automatic Speech Recognition—0
Out-of-Domain Generalization from a Single Source: An Uncertainty Quantification Approach—0
Overcoming Data Scarcity in Multi-Dialectal Arabic ASR via Whisper Fine-Tuning—0
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts—0
Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership—0
Overfitting Mechanism and Avoidance in Deep Neural Networks—0
OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models—0
OWSM-Biasing: Contextualizing Open Whisper-Style Speech Models for Automatic Speech Recognition with Dynamic Vocabulary—0
OWSM-CTC: An Open Encoder-Only Speech Foundation Model for Speech Recognition, Translation, and Language Identification—0
OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer—0
MAC: A unified framework boosting low resource automatic speech recognition—0
papago: A Machine Translation Service with Word Sense Disambiguation and Currency Conversion—0
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition—0
Paraformer-v2: An improved non-autoregressive transformer for noise-robust speech recognition—0
Paralinguistic Privacy Protection at the Edge—0
Parallel Composition of Weighted Finite-State Transducers—0
Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)—0
Parallel Corpus for Japanese Spoken-to-Written Style Conversion—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