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 26512700 of 6433 papers

TitleStatusHype
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework0
Combining Punctuation and Disfluency Prediction: An Empirical Study0
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior0
A Scalable Architecture For Web Deployment of Spoken Dialogue Systems0
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks0
Generating More Specific Questions for Acquiring Attributes of Unknown Concepts from Users0
Generating Robust Audio Adversarial Examples using Iterative Proportional Clipping0
Generating sets of related sentences from input seed features0
Generating Synthetic Audio Data for Attention-Based Speech Recognition Systems0
Generating Synthetic Clinical Speech Data through Simulated ASR Deletion Error0
Generating Task-Pertinent sorted Error Lists for Speech Recognition0
Generation and Pruning of Pronunciation Variants to Improve ASR Accuracy0
G\'en\'eration des prononciations de noms propres \`a l'aide des Champs Al\'eatoires Conditionnels (Pronunciation generation for proper names using Conditional Random Fields) [in French]0
CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition0
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice0
Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges0
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech0
Generative Context-aware Fine-tuning of Self-supervised Speech Models0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space0
Generative Goal-Driven User Simulation for Dialog Management0
Enhancing Speech Recognition Decoding via Layer Aggregation0
Generative linguistic representation for spoken language identification0
A Novel End-to-End CAPT System for L2 Children Learners0
Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting0
GeneSys: Enabling Continuous Learning through Neural Network Evolution in Hardware0
Compact, Efficient and Unlimited Capacity: Language Modeling with Compressed Suffix Trees0
Geometric Understanding of Deep Learning0
German-Arabic Speech-to-Speech Translation for Psychiatric Diagnosis0
Gesture-Aware Zero-Shot Speech Recognition for Patients with Language Disorders0
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders0
GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System0
Gibbs Sampling with Low-Power Spiking Digital Neurons0
Enhancing Speech Instruction Understanding and Disambiguation in Robotics via Speech Prosody0
Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models0
Enhancing Neural Spoken Language Recognition: An Exploration with Multilingual Datasets0
An Outlyingness Matrix for Multivariate Functional Data Classification0
Advances in All-Neural Speech Recognition0
Globally Normalising the Transducer for Streaming Speech Recognition0
An Efficient Pre-processing Method to Eliminate Adversarial Effects0
Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition0
Global SNR Estimation of Speech Signals using Entropy and Uncertainty Estimates from Dropout Networks0
GNCformer Enhanced Self-attention for Automatic Speech Recognition0
Goal-driven text descriptions for images0
Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers0
Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks0
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages0
Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems0
Enhancing Multilingual Speech Recognition through Language Prompt Tuning and Frame-Level Language Adapter0
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks0
Enhancing Multilingual ASR for Unseen Languages via Language Embedding Modeling0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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