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 851–900 of 6433 papers

TitleStatusHype
Enhancing CTC-based speech recognition with diverse modeling units—0
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task LearningCode5
Text Injection for Neural Contextual Biasing—0
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition—0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing—0
Keyword-Guided Adaptation of Automatic Speech Recognition—0
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping—0
Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision—0
Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach—0
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization—0
YODAS: Youtube-Oriented Dataset for Audio and Speech—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities—0
Augmented Conversation with Embedded Speech-Driven On-the-Fly Referencing in AR—0
NUTS, NARS, and Speech—0
Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation—0
TransVIP: Speech to Speech Translation System with Voice and Isochrony PreservationCode2
A Variance-Preserving Interpolation Approach for Diffusion Models with Applications to Single Channel Speech Enhancement and RecognitionCode1
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation ModelsCode3
A Survey on Vision-Language-Action Models for Embodied AICode4
Let's Fuse Step by Step: A Generative Fusion Decoding Algorithm with LLMs for Multi-modal Text RecognitionCode2
Contextualized Automatic Speech Recognition with Dynamic Vocabulary—0
ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos—0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish—0
Non-autoregressive real-time Accent Conversion model with voice cloning—0
FairLENS: Assessing Fairness in Law Enforcement Speech Recognition—0
Mamba in Speech: Towards an Alternative to Self-AttentionCode2
Could a Computer Architect Understand our Brain?—0
FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher informationCode2
Continuous Sign Language Recognition with Adapted Conformer via Unsupervised Pretraining—0
Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge SystemCode4
Listen Again and Choose the Right Answer: A New Paradigm for Automatic Speech Recognition with Large Language Models—0
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings—0
No More Mumbles: Enhancing Robot Intelligibility through Speech AdaptationCode0
Towards Evaluating the Robustness of Automatic Speech Recognition Systems via Audio Style Transfer—0
Sonos Voice Control Bias Assessment Dataset: A Methodology for Demographic Bias Assessment in Voice Assistants—0
Investigating the 'Autoencoder Behavior' in Speech Self-Supervised Models: a focus on HuBERT's Pretraining—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
SoccerNet-Echoes: A Soccer Game Audio Commentary DatasetCode1
Watch Your Mouth: Silent Speech Recognition with Depth SensingCode1
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
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation ModelsCode1
Audio-Visual Speech Recognition based on Regulated Transformer and Spatio-Temporal Fusion Strategy for Driver Assistive SystemsCode0
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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