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 1201–1250 of 6433 papers

TitleStatusHype
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech RecognitionCode0
Enhancing Large Language Model-based Speech Recognition by Contextualization for Rare and Ambiguous Words—0
DPSNN: Spiking Neural Network for Low-Latency Streaming Speech Enhancement—0
Style-Talker: Finetuning Audio Language Model and Style-Based Text-to-Speech Model for Fast Spoken Dialogue Generation—0
Audio Enhancement for Computer Audition -- An Iterative Training Paradigm Using Sample Importance—0
Cross-Lingual Conversational Speech Summarization with Large Language Models—0
Enhancing Dialogue Speech Recognition with Robust Contextual Awareness via Noise Representation Learning—0
VQ-CTAP: Cross-Modal Fine-Grained Sequence Representation Learning for Speech Processing—0
Improving Whisper's Recognition Performance for Under-Represented Language Kazakh Leveraging Unpaired Speech and Text—0
HydraFormer: One Encoder For All Subsampling RatesCode0
Preserving spoken content in voice anonymisation with character-level vocoder conditioningCode0
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
StreamVoice+: Evolving into End-to-end Streaming Zero-shot Voice Conversion—0
The NPU-ASLP System Description for Visual Speech Recognition in CNVSRC 2024Code0
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
On the Problem of Text-To-Speech Model Selection for Synthetic Data Generation in Automatic Speech Recognition—0
Towards interfacing large language models with ASR systems using confidence measures and prompting—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
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
Scaling A Simple Approach to Zero-Shot Speech Recognition—0
Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions—0
On the Effect of Purely Synthetic Training Data for Different Automatic Speech Recognition Architectures—0
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
Robustness of Speech Separation Models for Similar-pitch Speakers—0
Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization—0
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance—0
GE2E-AC: Generalized End-to-End Loss Training for Accent Classification—0
Handling Numeric Expressions in Automatic Speech Recognition—0
A light-weight and efficient punctuation and word casing prediction model for on-device streaming ASR—0
Robust ASR Error Correction with Conservative Data Filtering—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
Identifying Speakers in Dialogue Transcripts: A Text-based Approach Using Pretrained Language ModelsCode0
The VoicePrivacy 2022 Challenge: Progress and Perspectives in Voice Anonymisation—0
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
CUSIDE-array: A Streaming Multi-Channel End-to-End Speech Recognition System with Realistic Evaluations—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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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