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

Showing 451500 of 6433 papers

TitleStatusHype
DENOASR: Debiasing ASRs through Selective Denoising0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap0
AlignVSR: Audio-Visual Cross-Modal Alignment for Visual Speech RecognitionCode1
Moonshine: Speech Recognition for Live Transcription and Voice CommandsCode9
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation0
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding0
Ichigo: Mixed-Modal Early-Fusion Realtime Voice AssistantCode7
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach0
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-AttentionCode1
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup0
Roadmap towards Superhuman Speech Understanding using Large Language Models0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation0
Computational Approaches to Arabic-English Code-Switching0
EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation LearningCode1
Investigation of Speaker Representation for Target-Speaker Speech Processing0
A Framework for Adapting Human-Robot Interaction to Diverse User GroupsCode0
In-Materia Speech Recognition0
Character-aware audio-visual subtitling in context0
State of NLP in Kenya: A Survey0
SLAM-AAC: Enhancing Audio Captioning with Paraphrasing Augmentation and CLAP-Refine through LLMs0
Automatic Speech Recognition with BERT and CTC Transformers: A Review0
UniGlyph: A Seven-Segment Script for Universal Language Representation0
Enhancing Indonesian Automatic Speech Recognition: Evaluating Multilingual Models with Diverse Speech Variabilities0
Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models0
A two-stage transliteration approach to improve performance of a multilingual ASR0
Advocating Character Error Rate for Multilingual ASR Evaluation0
The USTC-NERCSLIP Systems for the CHiME-8 MMCSG Challenge0
Automatic Screening for Children with Speech Disorder using Automatic Speech Recognition: Opportunities and Challenges0
Incorporating Talker Identity Aids With Improving Speech Recognition in Adversarial Environments0
CR-CTC: Consistency regularization on CTC for improved speech recognition0
Punctuation Prediction for Polish Texts using Transformers0
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition0
The OCON model: an old but gold solution for distributable supervised classificationCode0
Enhancement of Dysarthric Speech Reconstruction by Contrastive Learning0
The OCON model: an old but green solution for distributable supervised classification for acoustic monitoring in smart cities0
Reverb: Open-Source ASR and Diarization from Rev0
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
Team MTS @ AutoMin 2021: An Overview of Existing Summarization Approaches and Comparison to Unsupervised Summarization Techniques0
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and ChallengesCode0
Convolutional Variational Autoencoders for Spectrogram Compression in Automatic Speech Recognition0
HAINAN: Fast and Accurate Transducer for Hybrid-Autoregressive ASR0
Algorithms For Automatic Accentuation And Transcription Of Russian Texts In Speech Recognition Systems0
A Pilot Study of Applying Sequence-to-Sequence Voice Conversion to Evaluate the Intelligibility of L2 Speech Using a Native Speaker's Shadowings0
Efficient Streaming LLM for Speech Recognition0
Spoken Grammar Assessment Using LLM0
End-to-End Speech Recognition with Pre-trained Masked Language Model0
Recent Advances in Speech Language Models: A SurveyCode2
Automatic Speech Recognition for the Ika Language0
VHASR: A Multimodal Speech Recognition System With Vision HotwordsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified