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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.

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Papers

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Showing 1301–1350 of 6433 papers

TitleStatusHype
Decoder-only Architecture for Streaming End-to-end Speech Recognition—0
Acoustic Feature Mixup for Balanced Multi-aspect Pronunciation Assessment—0
Perception of Phonological Assimilation by Neural Speech Recognition Models—0
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices—0
InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions—0
An Adapter-Based Unified Model for Multiple Spoken Language Processing Tasks—0
Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries—0
DASB -- Discrete Audio and Speech Benchmark—0
Joint vs Sequential Speaker-Role Detection and Automatic Speech Recognition for Air-traffic Control—0
ManWav: The First Manchu ASR Model—0
Children's Speech Recognition through Discrete Token Enhancement—0
Transcribe, Align and Segment: Creating speech datasets for low-resource languages—0
Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting—0
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of SpeechCode0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Performant ASR Models for Medical Entities in Accented Speech—0
Unsupervised Online Continual Learning for Automatic Speech RecognitionCode0
Self-Train Before You TranscribeCode0
Imperceptible Rhythm Backdoor Attacks: Exploring Rhythm Transformation for Embedding Undetectable Vulnerabilities on Speech Recognition—0
Optimized Speculative Sampling for GPU Hardware AcceleratorsCode0
Automatic Speech Recognition for Biomedical Data in Bengali Language—0
Large Language Models for Dysfluency Detection in Stuttered Speech—0
CoSTA: Code-Switched Speech Translation using Aligned Speech-Text Interleaving—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
Trading Devil: Robust backdoor attack via Stochastic investment models and Bayesian approach—0
An efficient text augmentation approach for contextualized Mandarin speech recognition—0
Learning Language Structures through Grounding—0
Optimizing Byte-level Representation for End-to-end ASR—0
On the Evaluation of Speech Foundation Models for Spoken Language Understanding—0
Inclusive ASR for Disfluent Speech: Cascaded Large-Scale Self-Supervised Learning with Targeted Fine-Tuning and Data Augmentation—0
CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge—0
Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition—0
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR—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
Language Complexity and Speech Recognition Accuracy: Orthographic Complexity Hurts, Phonological Complexity Doesn'tCode0
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
The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments—0
LASER: Learning by Aligning Self-supervised Representations of Speech for Improving Content-related TasksCode0
Exploring Spoken Language Identification Strategies for Automatic Transcription of Multilingual Broadcast and Institutional Speech—0
Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment—0
Neural Blind Source Separation and Diarization for Distant Speech Recognition—0
Towards Unsupervised Speech Recognition Without Pronunciation ModelsCode0
DualVC 3: Leveraging Language Model Generated Pseudo Context for End-to-end Low Latency Streaming Voice Conversion—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
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Transformer-based Model for ASR N-Best Rescoring and Rewriting—0
PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models—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