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 1051–1100 of 6433 papers

TitleStatusHype
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities—0
Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models—0
A two-stage transliteration approach to improve performance of a multilingual ASR—0
Advocating Character Error Rate for Multilingual ASR Evaluation—0
The USTC-NERCSLIP Systems for the CHiME-8 MMCSG Challenge—0
Incorporating Talker Identity Aids With Improving Speech Recognition in Adversarial Environments—0
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges—0
CR-CTC: Consistency regularization on CTC for improved speech recognition—0
Punctuation Prediction for Polish Texts using Transformers—0
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition—0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities—0
The OCON model: an old but gold solution for distributable supervised classificationCode0
Enhancement of Dysarthric Speech Reconstruction by Contrastive Learning—0
Reverb: Open-Source ASR and Diarization from Rev—0
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and ChallengesCode0
Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques—0
HAINAN: Fast and Accurate Transducer for Hybrid-Autoregressive ASR—0
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems—0
A Pilot Study of Applying Sequence-to-Sequence Voice Conversion to Evaluate the Intelligibility of L2 Speech Using a Native Speaker's Shadowings—0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition—0
Spoken Grammar Assessment Using LLM—0
Efficient Streaming LLM for Speech Recognition—0
Automatic Speech Recognition for the Ika Language—0
End-to-End Speech Recognition with Pre-trained Masked Language Model—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding—0
Quantitative Analysis of Audio-Visual Tasks: An Information-Theoretic Perspective—0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility—0
CoT-ST: Enhancing LLM-based Speech Translation with Multimodal Chain-of-Thought—0
Efficient Long-Form Speech Recognition for General Speech In-Context Learning—0
Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models—0
A GEN AI Framework for Medical Note Generation—0
Unveiling the Role of Pretraining in Direct Speech Translation—0
Paraformer-v2: An improved non-autoregressive transformer for noise-robust speech recognition—0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not—0
Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-LabelingCode0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens—0
Revisiting Acoustic Features for Robust ASR—0
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—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