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 60016050 of 6433 papers

TitleStatusHype
EAT: Enhanced ASR-TTS for Self-supervised Speech RecognitionCode0
Comparison and Analysis of New Curriculum Criteria for End-to-End ASRCode0
An Effective Transformer-based Contextual Model and Temporal Gate Pooling for Speaker IdentificationCode0
Improved Speech Enhancement with the Wave-U-NetCode0
A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognitionCode0
Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics ProcessingCode0
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
Segmentation-Free Streaming Machine TranslationCode0
Training dynamic models using early exits for automatic speech recognition on resource-constrained devicesCode0
Training Efficient CNNS: Tweaking the Nuts and Bolts of Neural Networks for Lighter, Faster and Robust ModelsCode0
Selective Attention Merging for low resource tasks: A case study of Child ASRCode0
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context ModelingCode0
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer LearningCode0
Evaluating Variants of wav2vec 2.0 on Affective Vocal Burst TasksCode0
Addressing Pitfalls in Auditing Practices of Automatic Speech Recognition Technologies: A Case Study of People with AphasiaCode0
CoMFLP: Correlation Measure based Fast Search on ASR Layer PruningCode0
Learning Human Pose Estimation Features with Convolutional NetworksCode0
Self-Attention Networks for Connectionist Temporal Classification in Speech RecognitionCode0
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech RecognitionCode0
Advancing Topic Segmentation of Broadcasted Speech with Multilingual Semantic EmbeddingsCode0
Probing Acoustic Representations for Phonetic PropertiesCode0
Acoustic absement in detail: Quantifying acoustic differences across time-series representations of speech dataCode0
Targeted Adversarial Examples for Black Box Audio SystemsCode0
ImportantAug: a data augmentation agent for speechCode0
Combining Residual Networks with LSTMs for LipreadingCode0
Self-Powered LLM Modality Expansion for Large Speech-Text ModelsCode0
Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal ModelsCode0
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive SystemsCode0
Speech Recognition Challenge in the Wild: Arabic MGB-3Code0
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
ProGRes: Prompted Generative Rescoring on ASR n-BestCode0
Evaluating Sequence-to-Sequence Models for Handwritten Text RecognitionCode0
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification TasksCode0
DeepEMO: Deep Learning for Speech Emotion RecognitionCode0
Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LMCode0
Evaluating robustness of You Only Hear Once(YOHO) Algorithm on noisy audios in the VOICe DatasetCode0
DeepCover: Advancing RNN Test Coverage and Online Error Prediction using State Machine ExtractionCode0
Analyzing the impact of speaker localization errors on speech separation for automatic speech recognitionCode0
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from SpeechCode0
Deep convolutional acoustic word embeddings using word-pair side informationCode0
Task Loss Estimation for Sequence PredictionCode0
Task Oriented Dialogue as a Catalyst for Self-Supervised Automatic Speech RecognitionCode0
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable ProjectionCode0
Evaluating context-invariance in unsupervised speech representationsCode0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech RecognitionCode0
Learning to detect dysarthria from raw speechCode0
Dysarthria Normalization via Local Lie Group Transformations for Robust ASRCode0
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language ModelsCode0
Leveraging Self-Supervised Models for Automatic Whispered Speech RecognitionCode0
Show:102550
← PrevPage 121 of 129Next →

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