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 1451–1500 of 6433 papers

TitleStatusHype
Conformer-1: Robust ASR via Large-Scale Semisupervised Bootstrapping—0
An inclusive review on deep learning techniques and their scope in handwriting recognition—0
The X-LANCE Technical Report for Interspeech 2024 Speech Processing Using Discrete Speech Unit Challenge—0
VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain—0
Transducers with Pronunciation-aware Embeddings for Automatic Speech Recognition—0
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian—0
Noise Masking Attacks and Defenses for Pretrained Speech Models—0
Transfer Learning from Whisper for Microscopic Intelligibility Prediction—0
Houston we have a Divergence: A Subgroup Performance Analysis of ASR Models—0
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language ModelsCode0
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition—0
LV-CTC: Non-autoregressive ASR with CTC and latent variable models—0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus—0
Extracting Biomedical Entities from Noisy Audio Transcripts—0
DANCER: Entity Description Augmented Named Entity Corrector for Automatic Speech Recognition—0
Grammatical vs Spelling Error Correction: An Investigation into the Responsiveness of Transformer-based Language Models using BART and MarianMT—0
Hierarchical Recurrent Adapters for Efficient Multi-Task Adaptation of Large Speech Models—0
Privacy-Preserving End-to-End Spoken Language Understanding—0
M^3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset—0
A Multimodal Approach to Device-Directed Speech Detection with Large Language Models—0
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception—0
BanglaNum -- A Public Dataset for Bengali Digit Recognition from Speech—0
Open Access NAO (OAN): a ROS2-based software framework for HRI applications with the NAO robot—0
Isometric Neural Machine Translation using Phoneme Count Ratio Reward-based Reinforcement Learning—0
AdaMER-CTC: Connectionist Temporal Classification with Adaptive Maximum Entropy Regularization for Automatic Speech Recognition—0
Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives—0
Energy-Based Models with Applications to Speech and Language Processing—0
Initial Decoding with Minimally Augmented Language Model for Improved Lattice Rescoring in Low Resource ASR—0
Hearing-Loss Compensation Using Deep Neural Networks: A Framework and Results From a Listening Test—0
More than words: Advancements and challenges in speech recognition for singing—0
SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different LanguagesCode0
Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer—0
Automatic Speech Recognition (ASR) for the Diagnosis of pronunciation of Speech Sound Disorders in Korean children—0
Skipformer: A Skip-and-Recover Strategy for Efficient Speech Recognition—0
Gujarati-English Code-Switching Speech Recognition using ensemble prediction of spoken language—0
Beyond the Labels: Unveiling Text-Dependency in Paralinguistic Speech Recognition Datasets—0
The evaluation of a code-switched Sepedi-English automatic speech recognition system—0
SCORE: Self-supervised Correspondence Fine-tuning for Improved Content RepresentationsCode0
Aligning Speech to Languages to Enhance Code-switching Speech Recognition—0
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech RecognitionCode0
Classist Tools: Social Class Correlates with Performance in NLP—0
A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain—0
RADIA -- Radio Advertisement Detection with Intelligent Analytics—0
Non-verbal information in spontaneous speech -- towards a new framework of analysis—0
AIx Speed: Playback Speed Optimization Using Listening Comprehension of Speech Recognition Models—0
What has LeBenchmark Learnt about French Syntax?—0
JEP-KD: Joint-Embedding Predictive Architecture Based Knowledge Distillation for Visual Speech Recognition—0
A Closer Look at Wav2Vec2 Embeddings for On-Device Single-Channel Speech Enhancement—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview—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