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 48514900 of 6433 papers

TitleStatusHype
Uncovering the Visual Contribution in Audio-Visual Speech Recognition0
Underspecification in Natural Language Understanding for Dialog Automation0
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning0
Understanding effect of speech perception in EEG based speech recognition systems0
Understanding Semantics from Speech Through Pre-training0
Understanding Shared Speech-Text Representations0
Understanding the Role of Self Attention for Efficient Speech Recognition0
Understanding Zero-shot Rare Word Recognition Improvements Through LLM Integration0
Unediting: Detecting Disfluencies Without Careful Transcripts0
UniBriVL: Robust Universal Representation and Generation of Audio Driven Diffusion Models0
Unidirectional Memory-Self-Attention Transducer for Online Speech Recognition0
UniEnc-CASSNAT: An Encoder-only Non-autoregressive ASR for Speech SSL Models0
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation0
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems0
Unified Guidelines and Resources for Arabic Dialect Orthography0
Unified Modeling of Multi-Domain Multi-Device ASR Systems0
Unified Modeling of Multi-Talker Overlapped Speech Recognition and Diarization with a Sidecar Separator0
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation0
Unified Semi-Supervised Pipeline for Automatic Speech Recognition0
Unified Speech-Text Pre-training for Speech Translation and Recognition0
Unified Speech-Text Pre-training for Speech Translation and Recognition0
Unifying Streaming and Non-streaming Zipformer-based ASR0
UniGlyph: A Seven-Segment Script for Universal Language Representation0
Unintended Memorization in Large ASR Models, and How to Mitigate It0
UniSpeech at scale: An Empirical Study of Pre-training Method on Large-Scale Speech Recognition Dataset0
Universal-2-TF: Robust All-Neural Text Formatting for ASR0
Universal Adversarial Perturbations for Speech Recognition Systems0
Universal Approximation with Quadratic Deep Networks0
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling0
Universal Fourier Attack for Time Series0
Universal Grapheme-to-Phoneme Prediction Over Latin Alphabets0
Universality of Deep Convolutional Neural Networks0
Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection0
UniverSLU: Universal Spoken Language Understanding for Diverse Tasks with Natural Language Instructions0
UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation0
UniX-Encoder: A Universal X-Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing0
Unmanned Aerial Vehicle Control Through Domain-based Automatic Speech Recognition0
Unsupervised Accent Adaptation Through Masked Language Model Correction Of Discrete Self-Supervised Speech Units0
Unsupervised Active Learning: Optimizing Labeling Cost-Effectiveness for Automatic Speech Recognition0
Unsupervised Adaptation for Statistical Machine Translation0
Unsupervised Adaptation with Domain Separation Networks for Robust Speech Recognition0
Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition0
Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR0
Unsupervised ASR via Cross-Lingual Pseudo-Labeling0
Unsupervised Automatic Speech Recognition: A Review0
Unsupervised Cross-Domain Singing Voice Conversion0
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces0
Unsupervised data selection for Speech Recognition with contrastive loss ratios0
Unsupervised Data Selection via Discrete Speech Representation for ASR0
Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition0
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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