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 36513700 of 6433 papers

TitleStatusHype
Predicting Compact Phrasal Rewrites with Large Language Models for ASR Post Editing0
Predicting disordered speech comprehensibility from Goodness of Pronunciation scores0
Predicting electrode array impedance after one month from cochlear implantation surgery0
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants0
Predicting ice flow using machine learning0
Predicting lexical skills from oral reading with acoustic measures0
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation0
Predicting Performance using Approximate State Space Model for Liquid State Machines0
Predicting positive transfer for improved low-resource speech recognition using acoustic pseudo-tokens0
Predicting Tasks in Goal-Oriented Spoken Dialog Systems using Semantic Knowledge Bases0
Prediction-Adaptation-Correction Recurrent Neural Networks for Low-Resource Language Speech Recognition0
Pr\'ediction de l'indexabilit\'e d'une transcription (Prediction of transcription indexability) [in French]0
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features0
Prediction of speech intelligibility with DNN-based performance measures0
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems0
Preliminary Study on SSCF-derived Polar Coordinate for ASR0
Preliminary Test of a Real-Time, Interactive Silent Speech Interface Based on Electromagnetic Articulograph0
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text0
Preserving Trees in Minimal Automata0
Pre-trained Model Representations and their Robustness against Noise for Speech Emotion Analysis0
Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning0
Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge0
Pre-training End-to-end ASR Models with Augmented Speech Samples Queried by Text0
Pre-Training for Query Rewriting in A Spoken Language Understanding System0
Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learning0
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data0
Pre-Training Transformers as Energy-Based Cloze Models0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework0
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?0
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants0
Privacy-Preserving Edge Speech Understanding with Tiny Foundation Models0
Privacy-Preserving End-to-End Spoken Language Understanding0
Privacy-Preserving Speech Representation Learning using Vector Quantization0
Privacy-preserving Voice Analysis via Disentangled Representations0
Private Language Model Adaptation for Speech Recognition0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition0
Probabilistic Dialogue Modeling for Speech-Enabled Assistive Technology0
Probabilistic Dialogue Models with Prior Domain Knowledge0
Probabilistic Hierarchical Clustering of Morphological Paradigms0
Probabilistic Integration of Partial Lexical Information for Noise Robust Haptic Voice Recognition0
Probabilistic Modelling of Morphologically Rich Languages0
Probing emergent geometry in speech models via replica theory0
Probing self-attention in self-supervised speech models for cross-linguistic differences0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge0
Probing Statistical Representations For End-To-End ASR0
Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models0
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers0
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding0
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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