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

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Showing 1401–1450 of 6433 papers

TitleStatusHype
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition—0
Non-autoregressive real-time Accent Conversion model with voice cloning—0
Could a Computer Architect Understand our Brain?—0
Continuous Sign Language Recognition with Adapted Conformer via Unsupervised Pretraining—0
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models—0
No More Mumbles: Enhancing Robot Intelligibility through Speech AdaptationCode0
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings—0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Investigating the 'Autoencoder Behavior' in Speech Self-Supervised Models: a focus on HuBERT's Pretraining—0
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants—0
SpeechVerse: A Large-scale Generalizable Audio Language Model—0
Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory DiseasesCode0
Large Language Models for Education: A Survey—0
Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech—0
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation—0
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive SystemsCode0
Open Implementation and Study of BEST-RQ for Speech Processing—0
Whispy: Adapting STT Whisper Models to Real-Time Environments—0
MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognition—0
Mixat: A Data Set of Bilingual Emirati-English SpeechCode0
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition—0
Deep Learning Models in Speech Recognition: Measuring GPU Energy Consumption, Impact of Noise and Model Quantization for Edge DeploymentCode0
Efficient Compression of Multitask Multilingual Speech Models—0
Low-resource speech recognition and dialect identification of Irish in a multi-task framework—0
Improving Membership Inference in ASR Model Auditing with Perturbed Loss Features—0
Sequence-to-sequence models in peer-to-peer learning: A practical application—0
Efficient Sample-Specific Encoder Perturbations—0
Active Learning with Task Adaptation Pre-training for Speech Emotion RecognitionCode0
Does Whisper understand Swiss German? An automatic, qualitative, and human evaluation—0
Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification—0
A cost minimization approach to fix the vocabulary size in a tokenizer for an End-to-End ASR system—0
Child Speech Recognition in Human-Robot Interaction: Problem Solved?—0
Developing Acoustic Models for Automatic Speech Recognition in Swedish—0
Automatic Speech Recognition System-Independent Word Error Rate Estimation—0
U2++ MoE: Scaling 4.7x parameters with minimal impact on RTF—0
Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices—0
Rethinking Processing Distortions: Disentangling the Impact of Speech Enhancement Errors on Speech Recognition Performance—0
Killkan: The Automatic Speech Recognition Dataset for Kichwa with Morphosyntactic InformationCode0
Breaking Walls: Pioneering Automatic Speech Recognition for Central Kurdish: End-to-End Transformer Paradigm—0
Exploring neural oscillations during speech perception via surrogate gradient spiking neural networksCode0
Semantically Corrected Amharic Automatic Speech RecognitionCode0
Efficient infusion of self-supervised representations in Automatic Speech Recognition—0
Learn2Talk: 3D Talking Face Learns from 2D Talking Face—0
Artificial Neural Networks to Recognize Speakers Division from Continuous Bengali Speech—0
Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional TrainingCode0
Anatomy of Industrial Scale Multilingual ASR—0
Resilience of Large Language Models for Noisy Instructions—0
Automatic Speech Recognition Advancements for Indigenous Languages of the Americas—0
Comparing Apples to Oranges: LLM-powered Multimodal Intention Prediction in an Object Categorization Task—0
An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified