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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 801–850 of 6433 papers

TitleStatusHype
Speech ReaLLM -- Real-time Streaming Speech Recognition with Multimodal LLMs by Teaching the Flow of Time—0
Multi-Modal Retrieval For Large Language Model Based Speech Recognition—0
AdaPTwin: Low-Cost Adaptive Compression of Product Twins in Transformers—0
Transcription-Free Fine-Tuning of Speech Separation Models for Noisy and Reverberant Multi-Speaker Automatic Speech Recognition—0
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related TasksCode0
The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments—0
Exploring Spoken Language Identification Strategies for Automatic Transcription of Multilingual Broadcast and Institutional Speech—0
Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn'tCode0
Improving child speech recognition with augmented child-like speech—0
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
Audio-conditioned phonemic and prosodic annotation for building text-to-speech models from unlabeled speech data—0
Refining Self-Supervised Learnt Speech Representation using Brain Activations—0
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion—0
Dual-Pipeline with Low-Rank Adaptation for New Language Integration in Multilingual ASR—0
Towards Unsupervised Speech Recognition Without Pronunciation ModelsCode0
Guiding Frame-Level CTC Alignments Using Self-knowledge DistillationCode0
Transformer-based Model for ASR N-Best Rescoring and Rewriting—0
PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models—0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding—0
Neural Blind Source Separation and Diarization for Distant Speech Recognition—0
Tag and correct: high precision post-editing approach to correction of speech recognition errors—0
Spoken Language Corpora Augmentation with Domain-Specific Voice-Cloned Speech—0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—0
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection—0
Reading Miscue Detection in Primary School through Automatic Speech Recognition—0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling—0
Synthetic Query Generation using Large Language Models for Virtual Assistants—0
Prompting Large Language Models with Audio for General-Purpose Speech SummarizationCode1
A Parameter-efficient Language Extension Framework for Multilingual ASR—0
Label-Looping: Highly Efficient Decoding for Transducers—0
MS-HuBERT: Mitigating Pre-training and Inference Mismatch in Masked Language Modelling methods for learning Speech Representations—0
Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of WhisperCode0
Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment—0
LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR—0
LLM-based speaker diarization correction: A generalizable approachCode1
Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis—0
Flexible Multichannel Speech Enhancement for Noise-Robust Frontend—0
Label-Synchronous Neural Transducer for E2E Simultaneous Speech TranslationCode1
To Distill or Not to Distill? On the Robustness of Robust Knowledge DistillationCode0
Hypernetworks for Personalizing ASR to Atypical Speech—0
BLSP-Emo: Towards Empathetic Large Speech-Language ModelsCode2
Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU—0
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Helsinki Speech Challenge 2024—0
LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech RecognitionCode1
Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and Gated Monolingual Datastores—0
Joint Beam Search Integrating CTC, Attention, and Transducer Decoders—0
Error-preserving Automatic Speech Recognition of Young English Learners' LanguageCode0
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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