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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 5501–5550 of 6433 papers

TitleStatusHype
Speech and Natural Language Processing Technologies for Pseudo-Pilot Simulator—0
Speech- and Text-driven Features for Automated Scoring of English Speaking Tasks—0
Speech Aware Dialog System Technology Challenge (DSTC11)—0
Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks—0
SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering—0
SpeechComposer: Unifying Multiple Speech Tasks with Prompt Composition—0
Speech Corpora Divergence Based Unsupervised Data Selection for ASR—0
Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language—0
Speech Corpus Spoken by Young-old, Old-old and Oldest-old Japanese—0
Speech Diarization and ASR with GMM—0
SpeechDPR: End-to-End Spoken Passage Retrieval for Open-Domain Spoken Question Answering—0
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features—0
Speech Emotion Recognition Using CNN and Its Use Case in Digital Healthcare—0
Speech Emotion Recognition Using Quaternion Convolutional Neural Networks—0
Speech-Enabled Computer-Aided Translation: A Satisfaction Survey with Post-Editor Trainees—0
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information—0
Speech Enhancement Modeling Towards Robust Speech Recognition System—0
Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks—0
Speech Enhancement Using Pitch Detection Approach For Noisy Environment—0
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention—0
Speech enhancement with frequency domain auto-regressive modeling—0
Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization—0
Speech inpainting: Context-based speech synthesis guided by video—0
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?—0
SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data—0
Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models—0
Speech-MLP: a simple MLP architecture for speech processing—0
SpeechMoE2: Mixture-of-Experts Model with Improved Routing—0
SpeechNet: Weakly Supervised, End-to-End Speech Recognition at Industrial Scale—0
Speech Pattern based Black-box Model Watermarking for Automatic Speech Recognition—0
Speech Rate Calculations with Short Utterances: A Study from a Speech-to-Speech, Machine Translation Mediated Map Task—0
Speech ReaLLM -- Real-time Streaming Speech Recognition with Multimodal LLMs by Teaching the Flow of Time—0
Speech Recognition-based Feature Extraction for Enhanced Automatic Severity Classification in Dysarthric Speech—0
Speech Recognition by Simply Fine-tuning BERT—0
Speech recognition for air traffic control via feature learning and end-to-end training—0
Speech Recognition for Automatically Assessing Afrikaans and isiXhosa Preschool Oral Narratives—0
Speech Recognition for Endangered and Extinct Samoyedic languages—0
Speech recognition for medical conversations—0
Speech Recognition for Tigrinya language Using Deep Neural Network Approach—0
Speech Recognition Front End Without Information Loss—0
Speech recognition in Alzheimer's disease with personal assistive robots—0
Speech Recognition: Keyword Spotting Through Image Recognition—0
語音辨識使用統計圖等化方法 (Speech Recognition Leveraging Histogram Equalization Methods) [In Chinese]—0
Speech Recognition on TV Series with Video-guided Post-Correction—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Speech Recognition Transformers: Topological-lingualism Perspective—0
Speech Recognition using EEG signals recorded using dry electrodes—0
Speech Recognition Web Services for Dutch—0
Speech Recognition with Augmented Synthesized Speech—0
Speech Recognition With LLMs Adapted to Disordered Speech Using Reinforcement Learning—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