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 26012650 of 6433 papers

TitleStatusHype
Front-End Adapter: Adapting Front-End Input of Speech based Self-Supervised Learning for Speech Recognition0
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply0
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization0
FT Speech: Danish Parliament Speech Corpus0
Full Attention Bidirectional Deep Learning Structure for Single Channel Speech Enhancement0
FullPack: Full Vector Utilization for Sub-Byte Quantized Inference on General Purpose CPUs0
Full-Rank No More: Low-Rank Weight Training for Modern Speech Recognition Models0
Full simulation on the dynamics of auditory synaptic fusion: Strong clustering of calcium channel might be the origin of the coherent release in the auditory hair cells0
FullStop:Punctuation and Segmentation Prediction for Dutch with Transformers0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model0
Fully Convolutional ASR for Less-Resourced Endangered Languages0
Fully Convolutional Speech Recognition0
Fully Learnable Front-End for Multi-Channel Acoustic Modeling using Semi-Supervised Learning0
Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices0
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech0
FunASR: A Fundamental End-to-End Speech Recognition Toolkit0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space0
Enhancing Speech Recognition Decoding via Layer Aggregation0
Fundamental Frequency Feature Normalization and Data Augmentation for Child Speech Recognition0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition0
A Novel End-to-End CAPT System for L2 Children Learners0
Efficiently Fusing Pretrained Acoustic and Linguistic Encoders for Low-resource Speech Recognition0
FusionFormer: Fusing Operations in Transformer for Efficient Streaming Speech Recognition0
Fusion Models for Improved Visual Captioning0
Future Vector Enhanced LSTM Language Model for LVCSR0
Future Word Contexts in Neural Network Language Models0
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR0
G2P Conversion of Proper Names Using Word Origin Information0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders0
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR0
Garnishing a phonetic dictionary for ASR intake0
Gated ConvNets for Letter-Based ASR0
Gated Embeddings in End-to-End Speech Recognition for Conversational-Context Fusion0
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices0
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition0
Gated Recurrent Neural Networks with Weighted Time-Delay Feedback0
Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody0
Gaussian Kernelized Self-Attention for Long Sequence Data and Its Application to CTC-based Speech Recognition0
Gaussian Quadrature for Kernel Features0
GE2E-AC: Generalized End-to-End Loss Training for Accent Classification0
GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems0
Gender and Dialect Bias in YouTube's Automatic Captions0
Gender Detection from Human Voice Using Tensor Analysis0
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models0
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space0
Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets0
An Outlyingness Matrix for Multivariate Functional Data Classification0
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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