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 43014350 of 6433 papers

TitleStatusHype
SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings0
Spell my name: keyword boosted speech recognition0
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation0
基於Sphinx 可快速個人化行動數字語音辨識系統 (Quickly Personalizable Mobile Digit Speech Recognition System Based on Sphinx) [In Chinese]0
Spike-Triggered Contextual Biasing for End-to-End Mandarin Speech Recognition0
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition0
SpliceOut: A Simple and Efficient Audio Augmentation Method0
Spoken Grammar Assessment Using LLM0
Spoken Language Corpora Augmentation with Domain-Specific Voice-Cloned Speech0
Spoken Language Translation for Polish0
SpokesBiz -- an Open Corpus of Conversational Polish0
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks0
SRU++: Pioneering Fast Recurrence with Attention for Speech Recognition0
SSHR: Leveraging Self-supervised Hierarchical Representations for Multilingual Automatic Speech Recognition0
SSNCSE_NLP@LT-EDI-ACL2022: Speech Recognition for Vulnerable Individuals in Tamil using pre-trained XLSR models0
Stabilising and accelerating light gated recurrent units for automatic speech recognition0
StableEmit: Selection Probability Discount for Reducing Emission Latency of Streaming Monotonic Attention ASR0
StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models0
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders0
Standard German Subtitling of Swiss German TV content: the PASSAGE Project0
StandUp4AI: A New Multilingual Dataset for Humor Detection in Stand-up Comedy Videos0
StarGAN-VC+ASR: StarGAN-based Non-Parallel Voice Conversion Regularized by Automatic Speech Recognition0
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University0
Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition0
State of NLP in Kenya: A Survey0
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition0
Statistical and Computational Guarantees for the Baum-Welch Algorithm0
Statistical Beamformer Exploiting Non-stationarity and Sparsity with Spatially Constrained ICA for Robust Speech Recognition0
Statistical Evaluation of Pronunciation Encoding0
Machine Learning Construction: implications to cybersecurity0
Statistical Machine Translation Improvement based on Phrase Selection0
Statistical Method of Building Dialect Language Models for ASR Systems0
Statistical Testing on ASR Performance via Blockwise Bootstrap0
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge0
STC speaker recognition systems for the NIST SRE 20210
Stepwise-Refining Speech Separation Network via Fine-Grained Encoding in High-order Latent Domain0
ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos0
Still not there? Comparing Traditional Sequence-to-Sequence Models to Encoder-Decoder Neural Networks on Monotone String Translation Tasks0
Stochastic Bi-Languages to model Dialogs0
STOP: A dataset for Spoken Task Oriented Semantic Parsing0
Strategies for improving low resource speech to text translation relying on pre-trained ASR models0
Strategies in Transfer Learning for Low-Resource Speech Synthesis: Phone Mapping, Features Input, and Source Language Selection0
Stream attention-based multi-array end-to-end speech recognition0
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition0
Streaming Audio-Visual Speech Recognition with Alignment Regularization0
Streaming automatic speech recognition with the transformer model0
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models0
Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification0
Streaming end-to-end multi-talker speech recognition0
Streaming end-to-end speech recognition with jointly trained neural feature enhancement0
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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