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 651–700 of 6433 papers

TitleStatusHype
wav2graph: A Framework for Supervised Learning Knowledge Graph from SpeechCode2
MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into LaTeX Formulas for Improved Readability—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval—0
The NPU-ASLP System Description for Visual Speech Recognition in CNVSRC 2024Code0
StreamVoice+: Evolving into End-to-end Streaming Zero-shot Voice Conversion—0
ALIF: Low-Cost Adversarial Audio Attacks on Black-Box Speech Platforms using Linguistic FeaturesCode1
SynesLM: A Unified Approach for Audio-visual Speech Recognition and Translation via Language Model and Synthetic Data—0
Sentence-wise Speech Summarization: Task, Datasets, and End-to-End Modeling with LM Knowledge Distillation—0
Towards interfacing large language models with ASR systems using confidence measures and prompting—0
The Llama 3 Herd of ModelsCode4
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Leveraging Self-Supervised Models for Automatic Whispered Speech RecognitionCode0
ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks—0
Improving noisy student training for low-resource languages in End-to-End ASR using CycleGAN and inter-domain losses—0
Enhancing Dysarthric Speech Recognition for Unseen Speakers via Prototype-Based AdaptationCode1
Dynamic Language Group-Based MoE: Enhancing Code-Switching Speech Recognition with Hierarchical RoutingCode1
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures—0
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions—0
Scaling A Simple Approach to Zero-Shot Speech Recognition—0
Sentiment Reasoning for HealthcareCode3
Coupling Speech Encoders with Downstream Text Models—0
A Comparative Analysis of Bilingual and Trilingual Wav2Vec Models for Automatic Speech Recognition in Multilingual Oral History Archives—0
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization—0
Quantifying the Role of Textual Predictability in Automatic Speech Recognition—0
Evolutionary Prompt Design for LLM-Based Post-ASR Error CorrectionCode1
Robustness of Speech Separation Models for Similar-pitch Speakers—0
dMel: Speech Tokenization made SimpleCode1
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
GE2E-AC: Generalized End-to-End Loss Training for Accent Classification—0
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance—0
Framework for Curating Speech Datasets and Evaluating ASR Systems: A Case Study for PolishCode1
Handling Numeric Expressions in Automatic Speech Recognition—0
Robust ASR Error Correction with Conservative Data Filtering—0
A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR—0
Low-Resourced Speech Recognition for Iu Mien Language via Weakly-Supervised Phoneme-based Multilingual Pre-training—0
Adaptive Cascading Network for Continual Test-Time AdaptationCode0
Morphosyntactic Analysis for CHILDES—0
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation—0
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language ModelsCode0
Vibravox: A Dataset of French Speech Captured with Body-conduction Audio SensorsCode1
Beyond Binary: Multiclass Paraphasia Detection with Generative Pretrained Transformers and End-to-End Models—0
Do You Act Like You Talk? Exploring Pose-based Driver Action Classification with Speech Recognition NetworksCode0
Leave No Knowledge Behind During Knowledge Distillation: Towards Practical and Effective Knowledge Distillation for Code-Switching ASR Using Realistic Data—0
Textless Dependency Parsing by Labeled Sequence PredictionCode0
Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation—0
Text-Based Detection of On-Hold Scripts in Contact Center CallsCode0
Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition SystemCode1
Speech Slytherin: Examining the Performance and Efficiency of Mamba for Speech Separation, Recognition, and SynthesisCode2
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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