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 401–450 of 6433 papers

TitleStatusHype
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory—0
Enhancing Code-Switching ASR Leveraging Non-Peaky CTC Loss and Deep Language Posterior Injection—0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation—0
k2SSL: A Faster and Better Framework for Self-Supervised Speech Representation Learning—0
Towards Maximum Likelihood Training for Transducer-based Streaming Speech Recognition—0
Scaling Speech-Text Pre-training with Synthetic Interleaved DataCode7
High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Transforming NLU with Babylon: A Case Study in Development of Real-time, Edge-Efficient, Multi-Intent Translation System for Automated Drive-Thru Ordering—0
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications—0
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language—0
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM—0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English—0
Whisper Finetuning on Nepali Language—0
A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children—0
Inter-linguistic Phonetic Composition (IPC): A Theoretical and Computational Approach to Enhance Second Language Pronunciation—0
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
BanglaDialecto: An End-to-End AI-Powered Regional Speech StandardizationCode0
WavChat: A Survey of Spoken Dialogue ModelsCode3
XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack DetectionCode1
DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and LocalizationCode0
Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems—0
Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data—0
Transferable Adversarial Attacks against ASR—0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions—0
CTC-Assisted LLM-Based Contextual ASR—0
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages—0
Unified Speech Recognition: A Single Model for Auditory, Visual, and Audiovisual InputsCode1
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation—0
Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO—0
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval—0
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?—0
Augmenting Polish Automatic Speech Recognition System With Synthetic Data—0
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription—0
Multilingual Standalone Trustworthy Voice-Based Social Network for Disaster Situations—0
Asynchronous Tool Usage for Real-Time Agents—0
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface ElectromyographyCode2
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
kNN For Whisper And Its Effect On Bias And Speaker Adaptation—0
A Survey on Speech Large Language Models—0
STTATTS: Unified Speech-To-Text And Text-To-Speech ModelCode1
Contextual Biasing to Improve Domain-specific Custom Vocabulary Audio Transcription without Explicit Fine-Tuning of Whisper Model—0
VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-TuningCode1
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models—0
DENOASR: Debiasing ASRs through Selective Denoising—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