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 44014450 of 6433 papers

TitleStatusHype
Model Blending for Text Classification0
Modeling Acoustic-Prosodic Cues for Word Importance Prediction in Spoken Dialogues0
Modeling Concept Dependencies in a Scientific Corpus0
Modeling Confidence in Sequence-to-Sequence Models0
Modeling Dependent Structure for Utterances in ASR Evaluation0
Modeling Interestingness with Deep Neural Networks0
Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no access to speech data0
Modeling speech recognition and synthesis simultaneously: Encoding and decoding lexical and sublexical semantic information into speech with no direct access to speech data0
Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities0
Modeling State-Conditional Observation Distribution using Weighted Stereo Samples for Factorial Speech Processing Models0
Model Interpolation with Trans-dimensional Random Field Language Models for Speech Recognition0
Modelling Human Clarification Strategies0
Modelling prosodic structure using Artificial Neural Networks0
Modelling word learning and recognition using visually grounded speech0
Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition0
Modular Domain Adaptation for Conformer-Based Streaming ASR0
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model0
Modular Hybrid Autoregressive Transducer0
Modular Representation of Layered Neural Networks0
MoHAVE: Mixture of Hierarchical Audio-Visual Experts for Robust Speech Recognition0
MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognition0
Monaural Multi-Talker Speech Recognition using Factorial Speech Processing Models0
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries0
Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition0
Monolingual Recognizers Fusion for Code-switching Speech Recognition0
Monotonic segmental attention for automatic speech recognition0
MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition0
More Speaking or More Speakers?0
More than words: Advancements and challenges in speech recognition for singing0
Morfessor 2.0: Toolkit for statistical morphological segmentation0
Morphological Segmentation for Keyword Spotting0
Morphosyntactic Analysis for CHILDES0
Morpho-Syntactic Study of Errors from Speech Recognition System0
Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments0
Moshi Moshi? A Model Selection Hijacking Adversarial Attack0
Motivations, challenges, and perspectives for the development of an Automatic Speech Recognition System for the under-resourced Ngiemboon Language0
Moving Toward High Precision Dynamical Modelling in Hidden Markov Models0
MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Models0
MSAT: Biologically Inspired Multi-Stage Adaptive Threshold for Conversion of Spiking Neural Networks0
MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR0
MS-HuBERT: Mitigating Pre-training and Inference Mismatch in Masked Language Modelling methods for learning Speech Representations0
MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research0
MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events0
MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems0
MTL-SLT: Multi-Task Learning for Spoken Language Tasks0
Mu^2SLAM: Multitask, Multilingual Speech and Language Models0
Multi-Accent Adaptation based on Gate Mechanism0
Multi-channel Conversational Speaker Separation via Neural Diarization0
Multichannel End-to-end Speech Recognition0
Show:102550
← PrevPage 89 of 129Next →

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