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 44514500 of 6433 papers

TitleStatusHype
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation0
Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature0
Multi-Channel Multi-Speaker ASR Using Target Speaker's Solo Segment0
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget0
Multi-Channel Transformer Transducer for Speech Recognition0
Multiclass ASMA vs Targeted PGD Attack in Image Segmentation0
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition0
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels0
Multi-Dialect Arabic Speech Recognition0
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model0
Multi-domain learning and generalization in dialog state tracking0
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition0
Multi-encoder multi-resolution framework for end-to-end speech recognition0
Multi-Frame Cross-Entropy Training for Convolutional Neural Networks in Speech Recognition0
Multi-Geometry Spatial Acoustic Modeling for Distant Speech Recognition0
Multi-Graph Decoding for Code-Switching ASR0
Multi-Head Decoder for End-to-End Speech Recognition0
Multi-head Monotonic Chunkwise Attention For Online Speech Recognition0
Multi-Head State Space Model for Speech Recognition0
Multi-Input Multi-Output Target-Speaker Voice Activity Detection For Unified, Flexible, and Robust Audio-Visual Speaker Diarization0
Multi-layer Attention Mechanism for Speech Keyword Recognition0
Multi-Level Modeling Units for End-to-End Mandarin Speech Recognition0
Multilingual and Cross-Lingual Intent Detection from Spoken Data0
Multilingual and crosslingual speech recognition using phonological-vector based phone embeddings0
Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study0
Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer0
Multilingual Contextual Adapters To Improve Custom Word Recognition In Low-resource Languages0
Multilingual End-to-End Speech Recognition with A Single Transformer on Low-Resource Languages0
Multilingual End-to-End Speech Translation0
Multilingual Parallel Corpus for Global Communication Plan0
Multilingual self-supervised speech representations improve the speech recognition of low-resource African languages with codeswitching0
Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling0
Multilingual Speech Recognition for Low-Resource Indian Languages using Multi-Task conformer0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes0
Multilingual Speech Recognition With A Single End-To-End Model0
Multilingual Speech Recognition with Corpus Relatedness Sampling0
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 20210
Multilingual Speech Translation with Unified Transformer: Huawei Noah’s Ark Lab at IWSLT 20210
Multilingual Standalone Trustworthy Voice-Based Social Network for Disaster Situations0
Multilingual Training and Cross-lingual Adaptation on CTC-based Acoustic Model0
Multilingual training set selection for ASR in under-resourced Malian languages0
Multilingual Transfer Learning for Children Automatic Speech Recognition0
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages0
Multilingual Word Error Rate Estimation: e-WER30
Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models0
Multimodal and Multiresolution Speech Recognition with Transformers0
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding0
Multimodal Audio-textual Architecture for Robust Spoken Language Understanding0
Multimodal Comparable Corpora as Resources for Extracting Parallel Data: Parallel Phrases Extraction0
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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
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord 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