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 35013550 of 6433 papers

TitleStatusHype
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition0
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition0
Echo State Speech Recognition0
Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics0
Do End-to-End Speech Recognition Models Care About Context?0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems0
Deep Learning based Multi-Source Localization with Source Splitting and its Effectiveness in Multi-Talker Speech Recognition0
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation0
Improving speech recognition models with small samples for air traffic control systems0
End-to-End Automatic Speech Recognition with Deep Mutual Learning0
Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows0
Jira: a Kurdish Speech Recognition System Designing and Building Speech Corpus and Pronunciation Lexicon0
Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classification0
Personalization Strategies for End-to-End Speech Recognition Systems0
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT0
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition0
Transformer-Based Approaches for Automatic Music TranscriptionCode0
Multimodal Punctuation Prediction with Contextual Dropout0
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-training and Its Application to Children's ASR0
Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understanding0
Hybrid phonetic-neural model for correction in speech recognition systemsCode0
Content-Aware Speaker Embeddings for Speaker Diarisation0
End-to-end Audio-visual Speech Recognition with ConformersCode1
Transformer Language Models with LSTM-based Cross-utterance Information RepresentationCode1
An Investigation of End-to-End Models for Robust Speech RecognitionCode1
Dompteur: Taming Audio Adversarial ExamplesCode1
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation0
Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers0
NUVA: A Naming Utterance Verifier for Aphasia Treatment0
Sparsification via Compressed Sensing for Automatic Speech Recognition0
BembaSpeech: A Speech Recognition Corpus for the Bemba LanguageCode1
Train your classifier first: Cascade Neural Networks Training from upper layers to lower layers0
Bayesian Transformer Language Models for Speech Recognition0
End-to-End Multi-Channel Transformer for Speech Recognition0
Effects of Layer Freezing on Transferring a Speech Recognition System to Under-resourced LanguagesCode0
Time-Domain Speech Extraction with Spatial Information and Multi Speaker Conditioning Mechanism0
A bandit approach to curriculum generation for automatic speech recognition0
Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASR0
Multi-Task Self-Supervised Pre-Training for Music Classification0
Intermediate Loss Regularization for CTC-based Speech Recognition0
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy0
Confusion2vec 2.0: Enriching Ambiguous Spoken Language Representations with SubwordsCode0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework0
The Multilingual TEDx Corpus for Speech Recognition and Translation0
WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition ToolkitCode3
Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition0
On Scaling Contrastive Representations for Low-Resource Speech Recognition0
Robust Attack Detection Approach for IIoT Using Ensemble Classifier0
Speech Recognition by Simply Fine-tuning BERT0
BCN2BRNO: ASR System Fusion for Albayzin 2020 Speech to Text Challenge0
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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