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 24012450 of 6433 papers

TitleStatusHype
Monolingual Recognizers Fusion for Code-switching Speech Recognition0
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss0
Avoid Overthinking in Self-Supervised Models for Speech Recognition0
Adapting self-supervised models to multi-talker speech recognition using speaker embeddings0
A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings0
Speech-text based multi-modal training with bidirectional attention for improved speech recognitionCode0
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain0
A Preliminary Study on Automated Speaking Assessment of English as a Second Language (ESL) Students0
FusionFormer: Fusing Operations in Transformer for Efficient Streaming Speech Recognition0
An analysis of degenerating speech due to progressive dysarthria on ASR performance0
Modular Hybrid Autoregressive Transducer0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Audio-Visual Speech Enhancement and Separation by Utilizing Multi-Modal Self-Supervised Embeddings0
Joint Audio/Text Training for Transformer Rescorer of Streaming Speech Recognition0
Fast and parallel decoding for transducer0
Structured State Space Decoder for Speech Recognition and Synthesis0
Predicting Multi-Codebook Vector Quantization Indexes for Knowledge Distillation0
Blank Collapse: Compressing CTC emission for the faster decodingCode0
DuDe: Dual-Decoder Multilingual ASR for Indian Languages using Common Label Set0
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers0
Application of Knowledge Distillation to Multi-task Speech Representation Learning0
End-to-end Spoken Language Understanding with Tree-constrained Pointer GeneratorCode0
Phonemic Representation and Transcription for Speech to Text Applications for Under-resourced Indigenous African Languages: The Case of Kiswahili0
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model0
Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem0
Filter and evolve: progressive pseudo label refining for semi-supervised automatic speech recognition0
Random Utterance Concatenation Based Data Augmentation for Improving Short-video Speech Recognition0
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech0
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance0
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation0
SAN: a robust end-to-end ASR model architecture0
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection0
Training Autoregressive Speech Recognition Models with Limited in-domain Supervision0
Contextual-Utterance Training for Automatic Speech Recognition0
Weight Averaging: A Simple Yet Effective Method to Overcome Catastrophic Forgetting in Automatic Speech Recognition0
On Out-of-Distribution Detection for Audio with Deep Nearest NeighborsCode0
Simulating realistic speech overlaps improves multi-talker ASR0
V-Cloak: Intelligibility-, Naturalness- & Timbre-Preserving Real-Time Voice Anonymization0
Iterative pseudo-forced alignment by acoustic CTC loss for self-supervised ASR domain adaptationCode0
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition0
Explicit Intensity Control for Accented Text-to-speech0
Evaluating context-invariance in unsupervised speech representationsCode0
UFO2: A unified pre-training framework for online and offline speech recognition0
Improving Speech-to-Speech Translation Through Unlabeled Text0
Four-in-One: A Joint Approach to Inverse Text Normalization, Punctuation, Capitalization, and Disfluency for Automatic Speech Recognition0
Reducing Language confusion for Code-switching Speech Recognition with Token-level Language DiarizationCode0
Monotonic segmental attention for automatic speech recognition0
Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead0
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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