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 601–650 of 6433 papers

TitleStatusHype
Espresso: A Fast End-to-end Neural Speech Recognition ToolkitCode1
Evaluating Speech Synthesis by Training Recognizers on Synthetic SpeechCode1
Approaching Deep Learning through the Spectral Dynamics of WeightsCode1
ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of KaldiCode1
A transfer learning based approach for pronunciation scoringCode1
Factorized Neural Transducer for Efficient Language Model AdaptationCode1
A Survey on Non-Autoregressive Generation for Neural Machine Translation and BeyondCode1
A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and EnglishCode1
Attack on practical speaker verification system using universal adversarial perturbationsCode1
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
Audio-Visual Efficient Conformer for Robust Speech RecognitionCode1
FlowerFormer: Empowering Neural Architecture Encoding using a Flow-aware Graph TransformerCode1
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning SimulationsCode1
Foundation TransformersCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Generative Pre-Training for Speech with Autoregressive Predictive CodingCode1
CLSRIL-23: Cross Lingual Speech Representations for Indic LanguagesCode1
Distilling Knowledge from Ensembles of Acoustic Models for Joint CTC-Attention End-to-End Speech RecognitionCode1
Global Normalization for Streaming Speech Recognition in a Modular FrameworkCode1
Google Crowdsourced Speech Corpora and Related Open-Source Resources for Low-Resource Languages and Dialects: An OverviewCode1
GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech RecognitionCode1
GPU-Accelerated WFST Beam Search Decoder for CTC-based Speech RecognitionCode1
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
HiFi-VC: High Quality ASR-Based Voice ConversionCode1
How2: A Large-scale Dataset for Multimodal Language UnderstandingCode1
Arabic Speech Emotion Recognition Employing Wav2vec2.0 and HuBERT Based on BAVED DatasetCode1
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data GenerationCode1
A Neural Morphological Analyzer for Arapaho Verbs Learned from a Finite State Transducer—0
A neural document language modeling framework for spoken document retrieval—0
A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation—0
A Neural Acoustic Echo Canceller Optimized Using An Automatic Speech Recognizer And Large Scale Synthetic Data—0
A network of deep neural networks for distant speech recognition—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A comprehensive analysis on attention models—0
An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition—0
An enhanced automatic speech recognition system for Arabic—0
A Deep Learning Approach for Similar Languages, Varieties and Dialects—0
An End-to-End Text-independent Speaker Verification Framework with a Keyword Adversarial Network—0
An End-to-End Speech Recognition for the Nepali Language—0
A Deep Generative Acoustic Model for Compositional Automatic Speech Recognition—0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition—0
A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems—0
A Comparison of Transformer, Convolutional, and Recurrent Neural Networks on Phoneme Recognition—0
An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling—0
Self-Supervised Learning for Multi-Channel Neural Transducer—0
An End-to-end Architecture of Online Multi-channel Speech Separation—0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition—0
A Deep Dive into Deep Cluster—0
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
An Empirical Study of Efficient ASR Rescoring with Transformers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN MMIPercentage error12.9—Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN BMMIPercentage error12.9—Unverified
9DNN MPEPercentage error12.9—Unverified
10Deep Speech + FSHPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
5Deep Speech 2Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified