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

Showing 36013650 of 6433 papers

TitleStatusHype
Phone Based Keyword Spotting for Transcribing Very Low Resource Languages0
Phoneme-aware Encoding for Prefix-tree-based Contextual ASR0
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models0
Phoneme Based Neural Transducer for Large Vocabulary Speech Recognition0
Phoneme-Based Persian Speech Recognition0
Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties0
Phoneme Recognition with Large Hierarchical Reservoirs0
Phone Merging For Code-Switched Speech Recognition0
Phoneme Set Design Using English Speech Database by Japanese for Dialogue-Based English CALL Systems0
Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario0
Phonemic and Graphemic Multilingual CTC Based Speech Recognition0
Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili0
Phonemic Transcription of Low-Resource Languages: To What Extent can Preprocessing be Automated?0
Phonetically Balanced Code-Mixed Speech Corpus for Hindi-English Automatic Speech Recognition0
Phonetic and Graphemic Systems for Multi-Genre Broadcast Transcription0
Phonetic-assisted Multi-Target Units Modeling for Improving Conformer-Transducer ASR system0
Phonetic-aware speaker embedding for far-field speaker verification0
Phonologically Informed Edit Distance Algorithms for Word Alignment with Low-Resource Languages0
Phonological Pun-derstanding0
Phrase Based Language Model for Statistical Machine Translation: Empirical Study0
Phrase Based Language Model For Statistical Machine Translation0
Phrase Model Training for Statistical Machine Translation with Word Lattices of Preprocessing Alternatives0
PickNet: Real-Time Channel Selection for Ad Hoc Microphone Arrays0
Piecewise convexity of artificial neural networks0
Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis0
PI-Whisper: Designing an Adaptive and Incremental Automatic Speech Recognition System for Edge Devices0
PizzaPal: Conversational Pizza Ordering using a High-Density Conversational AI Platform0
Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-190
pMCT: Patched Multi-Condition Training for Robust Speech Recognition0
PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition0
PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Features0
PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant using Interventional Radiology Workflow Analysis0
Policy Learning for Domain Selection in an Extensible Multi-domain Spoken Dialogue System0
Polyphonic pitch detection with convolutional recurrent neural networks0
PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models0
POMDP-based dialogue manager adaptation to extended domains0
Population Based Training for Data Augmentation and Regularization in Speech Recognition0
Portable Speech-to-Speech Translation on an Android Smartphone: The MFLTS System0
Positional Description for Numerical Normalization0
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network0
Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview0
Post-Stroke Speech Transcription Challenge (Task B): Correctness Detection in Anomia Diagnosis with Imperfect Transcripts0
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition0
PP-MeT: a Real-world Personalized Prompt based Meeting Transcription System0
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems0
Practical Speech Recognition with HTK0
Practice of the conformer enhanced AUDIO-VISUAL HUBERT on Mandarin and English0
Precise Detection of Speech Endpoints Dynamically: A Wavelet Convolution based approach0
Pre-Computable Multi-Layer Neural Network Language Models0
Predicting Causes of Reformulation in Intelligent Assistants0
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified