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 49515000 of 6433 papers

TitleStatusHype
Pre-training in Deep Reinforcement Learning for Automatic Speech Recognition0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data0
Pre-Training Transformers as Energy-Based Cloze Models0
Privacy attacks for automatic speech recognition acoustic models in a federated learning framework0
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?0
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants0
Privacy-Preserving Edge Speech Understanding with Tiny Foundation Models0
Privacy-Preserving End-to-End Spoken Language Understanding0
Privacy-Preserving Speech Representation Learning using Vector Quantization0
Privacy-preserving Voice Analysis via Disentangled Representations0
Private Language Model Adaptation for Speech Recognition0
Proactive Security: Embedded AI Solution for Violent and Abusive Speech Recognition0
Probabilistic Dialogue Modeling for Speech-Enabled Assistive Technology0
Probabilistic Dialogue Models with Prior Domain Knowledge0
Probabilistic Hierarchical Clustering of Morphological Paradigms0
Probabilistic Integration of Partial Lexical Information for Noise Robust Haptic Voice Recognition0
Probabilistic Modelling of Morphologically Rich Languages0
Probing emergent geometry in speech models via replica theory0
Probing self-attention in self-supervised speech models for cross-linguistic differences0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge0
Probing Statistical Representations For End-To-End ASR0
Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems0
Proceedings of the ISCA/ITG Workshop on Diversity in Large Speech and Language Models0
PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers0
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding0
Progress in Multilingual Speech Recognition for Low Resource Languages Kurmanji Kurdish, Cree and Inuktut0
Progressive Down-Sampling for Acoustic Encoding0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition0
Progressive Label Distillation: Learning Input-Efficient Deep Neural Networks0
Progressive Multi-Scale Self-Supervised Learning for Speech Recognition0
Progressive Residual Extraction based Pre-training for Speech Representation Learning0
Progressive unsupervised domain adaptation for ASR using ensemble models and multi-stage training0
Projection of Turn Completion in Incremental Spoken Dialogue Systems0
Prompt-based Content Scoring for Automated Spoken Language Assessment0
Promptformer: Prompted Conformer Transducer for ASR0
Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition0
Prompting Large Language Models with Speech Recognition Abilities0
Prompting Whisper for Improved Verbatim Transcription and End-to-end Miscue Detection0
Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition0
PronouncUR: An Urdu Pronunciation Lexicon Generator0
Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training0
Pronunciation-aware unique character encoding for RNN Transducer-based Mandarin speech recognition0
Pronunciation Dictionary-Free Multilingual Speech Synthesis by Combining Unsupervised and Supervised Phonetic Representations0
Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition0
Pronunciation Modeling of Foreign Words for Mandarin ASR by Considering the Effect of Language Transfer0
Pronunciation recognition of English phonemes /@/, /æ/, /A:/ and /2/ using Formants and Mel Frequency Cepstral Coefficients0
Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech0
Prosody in Cascade and Direct Speech-to-Text Translation: a case study on Korean Wh-Phrases0
Prosomarker: a prosodic analysis tool based on optimal pitch stylization and automatic syllabi fication0
Protecting gender and identity with disentangled speech representations0
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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