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
Training ASR models by Generation of Contextual Information0
Meta Learning for End-to-End Low-Resource Speech Recognition0
L2RS: A Learning-to-Rescore Mechanism for Automatic Speech Recognition0
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering0
Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural NetworksCode0
Towards Online End-to-end Transformer Automatic Speech Recognition0
A Bayesian Approach to Recurrence in Neural Networks0
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech ToolkitCode0
Recognizing long-form speech using streaming end-to-end models0
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognitionCode0
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition0
An Empirical Study of Efficient ASR Rescoring with Transformers0
A Transformer with Interleaved Self-attention and Convolution for Hybrid Acoustic ModelsCode0
RNN based Incremental Online Spoken Language Understanding0
Efficient Dynamic WFST Decoding for Personalized Language Models0
Analyzing ASR pretraining for low-resource speech-to-text translation0
Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model0
A practical two-stage training strategy for multi-stream end-to-end speech recognition0
Transformer-based Acoustic Modeling for Hybrid Speech Recognition0
Word-level Embeddings for Cross-Task Transfer Learning in Speech ProcessingCode0
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts0
Adversarial Example Detection by Classification for Deep Speech RecognitionCode0
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR0
AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks0
Signal Combination for Language Identification0
Predicting ice flow using machine learning0
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models0
End-to-End Speech Recognition: A review for the French Language0
Indian EmoSpeech Command Dataset: A dataset for emotion based speech recognition in the wildCode0
LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech RecognitionCode0
Multi-Talker MVDR Beamforming Based on Extended Complex Gaussian Mixture Model0
Detecting Multiple Speech Disfluencies using a Deep Residual Network with Bidirectional Long Short-Term Memory0
Lead2Gold: Towards exploiting the full potential of noisy transcriptions for speech recognition0
Transformer ASR with Contextual Block Processing0
MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition0
Analyzing Large Receptive Field Convolutional Networks for Distant Speech Recognition0
Transfer Learning for Algorithm Recommendation0
A Research Platform for Multi-Robot Dialogue with Humans0
VAIS ASR: Building a conversational speech recognition system using language model combination0
Query-by-example on-device keyword spotting0
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems0
One-To-Many Multilingual End-to-end Speech Translation0
Adapting a FrameNet Semantic Parser for Spoken Language Understanding Using Adversarial Learning0
A Case Study on Combining ASR and Visual Features for Generating Instructional Video Captions0
Modeling Confidence in Sequence-to-Sequence Models0
Distributed Learning of Deep Neural Networks using Independent Subnet TrainingCode0
SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition0
Convolutional Neural Networks for Speech Controlled Prosthetic Hands0
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System0
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid 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