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 601–650 of 6433 papers

TitleStatusHype
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
VoxHakka: A Dialectally Diverse Multi-speaker Text-to-Speech System for Taiwanese Hakka—0
Resource-Efficient Adaptation of Speech Foundation Models for Multi-Speaker ASR—0
A Framework for Synthetic Audio Conversations Generation using Large Language Models—0
Comparing Discrete and Continuous Space LLMs for Speech Recognition—0
Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognition—0
Progressive Residual Extraction based Pre-training for Speech Representation Learning—0
DCIM-AVSR : Efficient Audio-Visual Speech Recognition via Dual Conformer Interaction Module—0
Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
ProGRes: Prompted Generative Rescoring on ASR n-BestCode0
Advancing Multi-talker ASR Performance with Large Language Models—0
Measuring the Accuracy of Automatic Speech Recognition SolutionsCode0
Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation—0
CrisperWhisper: Accurate Timestamps on Verbatim Speech TranscriptionsCode4
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction—0
Beyond Levenshtein: Leveraging Multiple Algorithms for Robust Word Error Rate Computations And Granular Error ClassificationsCode0
Speech Recognition Transformers: Topological-lingualism Perspective—0
Literary and Colloquial Dialect Identification for Tamil using Acoustic Features—0
Automatic recognition and detection of aphasic natural speech—0
Self-supervised Speech Representations Still Struggle with African American Vernacular EnglishCode0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
Literary and Colloquial Tamil Dialect Identification—0
Studying the Effect of Audio Filters in Pre-Trained Models for Environmental Sound Classification—0
Focused Discriminative Training For Streaming CTC-Trained Automatic Speech Recognition Models—0
Developing vocal system impaired patient-aimed voice quality assessment approach using ASR representation-included multiple features—0
Positional Description for Numerical Normalization—0
Towards measuring fairness in speech recognition: Fair-Speech dataset—0
The State of Commercial Automatic French Legal Speech Recognition Systems and their Impact on Court Reporters et al—0
Improving Speech Recognition Error Prediction for Modern and Off-the-shelf Speech Recognizers—0
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition—0
Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directions—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Recording for Eyes, Not Echoing to Ears: Contextualized Spoken-to-Written Conversion of ASR Transcripts—0
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech RecognitionCode0
Enhancing Large Language Model-based Speech Recognition by Contextualization for Rare and Ambiguous Words—0
DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement—0
SER Evals: In-domain and Out-of-domain Benchmarking for Speech Emotion RecognitionCode1
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation—0
Cross-Lingual Conversational Speech Summarization with Large Language Models—0
Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
LI-TTA: Language Informed Test-Time Adaptation for Automatic Speech RecognitionCode1
VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing—0
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text—0
MooER: LLM-based Speech Recognition and Translation Models from Moore ThreadsCode3
Preserving spoken content in voice anonymisation with character-level vocoder conditioningCode0
HydraFormer: One Encoder For All Subsampling RatesCode0
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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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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