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 42514300 of 6433 papers

TitleStatusHype
Low-Resourced Speech Recognition for Iu Mien Language via Weakly-Supervised Phoneme-based Multilingual Pre-training0
Low Resource German ASR with Untranscribed Data Spoken by Non-native Children -- INTERSPEECH 2021 Shared Task SPAPL System0
Low-resource Low-footprint Wake-word Detection using Knowledge Distillation0
Low-Resource Machine Transliteration Using Recurrent Neural Networks of Asian Languages0
Low-resource speech recognition and dialect identification of Irish in a multi-task framework0
Low-Resource Speech-to-Text Translation0
Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions0
LRSpeech: Extremely Low-Resource Speech Synthesis and Recognition0
LRWR: Large-Scale Benchmark for Lip Reading in Russian language0
LSTM Acoustic Models Learn to Align and Pronounce with Graphemes0
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity0
LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition0
SHARP: An Adaptable, Energy-Efficient Accelerator for Recurrent Neural Network0
LUPET: Incorporating Hierarchical Information Path into Multilingual ASR0
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and Table Lookup0
LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications0
LV-CTC: Non-autoregressive ASR with CTC and latent variable models0
Lyrics-to-Audio Alignment by Unsupervised Discovery of Repetitive Patterns in Vowel Acoustics0
M^3AV: A Multimodal, Multigenre, and Multipurpose Audio-Visual Academic Lecture Dataset0
M3D-GAN: Multi-Modal Multi-Domain Translation with Universal Attention0
MAC-DO: An Efficient Output-Stationary GEMM Accelerator for CNNs Using DRAM Technology0
Machine Semiotics0
Machine Speech Chain with One-shot Speaker Adaptation0
Machine Unlearning: A Survey0
Macro-block dropout for improved regularization in training end-to-end speech recognition models0
MADI: Inter-domain Matching and Intra-domain Discrimination for Cross-domain Speech Recognition0
MAESTRO: Matched Speech Text Representations through Modality Matching0
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.00
Mai Ho'omāuna i ka 'Ai: Language Models Improve Automatic Speech Recognition in Hawaiian0
Make More of Your Data: Minimal Effort Data Augmentation for Automatic Speech Recognition and Translation0
Making Convolutional Networks Recurrent for Visual Sequence Learning0
Making Speech-Based Assistive Technology Work for a Real User0
Malayalam Speech Corpus: Design and Development for Dravidian Language0
M\'alr\'omur: A Manually Verified Corpus of Recorded Icelandic Speech0
MAM: Masked Acoustic Modeling for End-to-End Speech-to-Text Translation0
Mandarin-English Code-switching Speech Recognition with Self-supervised Speech Representation Models0
Mandarin-English Code-Switching Speech Recognition System for Specific Domain0
Manifold-Kernels Comparison in MKPLS for Visual Speech Recognition0
ManWav: The First Manchu ASR Model0
Mapping AI Arguments in Journalism Studies0
Mapping Diatopic and Diachronic Variation in Spoken Czech: The ORTOFON and DIALEKT Corpora0
Mapping Generative Models onto a Network of Digital Spiking Neurons0
Mapping Rules for Building a Tunisian Dialect Lexicon and Generating Corpora0
Markovian Discriminative Modeling for Dialog State Tracking0
Masked Audio Text Encoders are Effective Multi-Modal Rescorers0
Mask scalar prediction for improving robust automatic speech recognition0
Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction0
MASR: A Modular Accelerator for Sparse RNNs0
MASRI-HEADSET: A Maltese Corpus for Speech Recognition0
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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