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 64016433 of 6433 papers

TitleStatusHype
Mlphon: A Multifunctional Grapheme-Phoneme Conversion Tool Using Finite State TransducersCode0
MLS: A Large-Scale Multilingual Dataset for Speech ResearchCode0
Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic DataCode0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
FastEmit: Low-latency Streaming ASR with Sequence-level Emission RegularizationCode0
The implementation of a Deep Recurrent Neural Network Language Model on a Xilinx FPGACode0
FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech DataCode0
Towards Event Extraction from Speech with Contextual CluesCode0
DEVI: Open-source Human-Robot Interface for Interactive Receptionist SystemsCode0
SoccerChat: Integrating Multimodal Data for Enhanced Soccer Game UnderstandingCode0
Efficient Keyword Spotting by capturing long-range interactions with Temporal Lambda NetworksCode0
Efficient Ensemble for Multimodal Punctuation Restoration using Time-Delay Neural NetworkCode0
Continual Learning for Monolingual End-to-End Automatic Speech RecognitionCode0
Pansori: ASR Corpus Generation from Open Online Video ContentsCode0
Application of Word2vec in Phoneme RecognitionCode0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Revisiting Word Embedding for Contrasting MeaningCode0
Context-Aware Dialog Re-Ranking for Task-Oriented Dialog SystemsCode0
Parallel Blockwise Knowledge Distillation for Deep Neural Network CompressionCode0
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter ItCode0
Contaminated speech training methods for robust DNN-HMM distant speech recognitionCode0
SoK: A Modularized Approach to Study the Security of Automatic Speech Recognition SystemsCode0
What does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesisCode0
Parallel training of DNNs with Natural Gradient and Parameter AveragingCode0
Building DNN Acoustic Models for Large Vocabulary Speech RecognitionCode0
Unsupervised Online Continual Learning for Automatic Speech RecognitionCode0
What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-trainingCode0
Structured State Space Model Dynamics and Parametrization for Spiking Neural NetworksCode0
A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative statesCode0
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech RecognitionCode0
RNN-Transducer-based Losses for Speech Recognition on Noisy TargetsCode0
Detecting and Defending Against Adversarial Attacks on Automatic Speech Recognition via Diffusion ModelsCode0
Bringing NURC/SP to Digital Life: the Role of Open-source Automatic Speech Recognition ModelsCode0
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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