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 38513900 of 6433 papers

TitleStatusHype
Whispy: Adapting STT Whisper Models to Real-Time Environments0
Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision0
Whither the Priors for (Vocal) Interactivity?0
Who Are We Talking About? Handling Person Names in Speech Translation0
Who Are We Talking About? Handling Person Names in Speech Translation0
Who Needs Decoders? Efficient Estimation of Sequence-level Attributes0
Who Needs Words? Lexicon-Free Speech Recognition0
Why Does Decentralized Training Outperform Synchronous Training In The Large Batch Setting?0
Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?0
WideResNet with Joint Representation Learning and Data Augmentation for Cover Song Identification0
Wiki-En-ASR-Adapt: Large-scale synthetic dataset for English ASR Customization0
Will a Blind Model Hear Better? Advanced Audiovisual Recognition System with Brain-Like Compensating and Gating0
Without Further Ado: Direct and Simultaneous Speech Translation by AppTek in 20210
WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognition0
Word Alignment Modeling with Context Dependent Deep Neural Network0
Word-Based Dialog State Tracking with Recurrent Neural Networks0
Word-Embedding based Content Features for Automated Oral Proficiency Scoring0
Word-Free Spoken Language Understanding for Mandarin-Chinese0
Word-level confidence estimation for RNN transducers0
Word-Level Language Identification and Predicting Codeswitching Points in Swahili-English Language Data0
Word-level Speech Recognition with a Letter to Word Encoder0
Word Level Timestamp Generation for Automatic Speech Recognition and Translation0
Word Order Does Not Matter For Speech Recognition0
Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations0
Word Segmentation of Informal Arabic with Domain Adaptation0
Words Worth: Verbal Content and Hirability Impressions in YouTube Video Resumes0
Word Transduction for Addressing the OOV Problem in Machine Translation for Similar Resource-Scarce Languages0
XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition0
XJSA at SemEval-2017 Task 4: A Deep System for Sentiment Classification in Twitter0
XLAVS-R: Cross-Lingual Audio-Visual Speech Representation Learning for Noise-Robust Speech Perception0
XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese0
XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models0
XLST: Cross-lingual Self-training to Learn Multilingual Representation for Low Resource Speech Recognition0
XNOR-FORMER: Learning Accurate Approximations in Long Speech Transformers0
XTREME-S: Evaluating Cross-lingual Speech Representations0
XY Neural Networks0
YODAS: Youtube-Oriented Dataset for Audio and Speech0
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish0
Your voice is your voice: Supporting Self-expression through Speech Generation and LLMs in Augmented and Alternative Communication0
ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus0
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition0
Zara The Supergirl: An Empathetic Personality Recognition System0
Zero-resource Speech Translation and Recognition with LLMs0
Zero-Shot Automatic Pronunciation Assessment0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition0
Zero-shot Disfluency Detection for Indian Languages0
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens0
Zero-shot Learning for Speech Recognition with Universal Phonetic Model0
Zero-Shot Learning of Language Models for Describing Human Actions Based on Semantic Compositionality of Actions0
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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