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 46014650 of 6433 papers

TitleStatusHype
Neural Caption Generation for News Images0
Neural Collaborative Ranking0
Neural Compatibility Modeling with Attentive Knowledge Distillation0
Neural Dependency Coding inspired Multimodal Fusion0
NeuralEcho: A Self-Attentive Recurrent Neural Network For Unified Acoustic Echo Suppression And Speech Enhancement0
Neural-FST Class Language Model for End-to-End Speech Recognition0
Neural Hybrid Recommender: Recommendation needs collaboration0
Neural Information Retrieval: A Literature Review0
Neural Kalman Filtering for Speech Enhancement0
Neural Language Codes for Multilingual Acoustic Models0
Neural Language Model Pruning for Automatic Speech Recognition0
Neural Machine Translation for Multilingual Grapheme-to-Phoneme Conversion0
Neural Machine Translation with the Transformer and Multi-Source Romance Languages for the Biomedical WMT 2018 task0
Neural Methods for Effective, Efficient, and Exposure-Aware Information Retrieval0
Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration0
Neural Morphological Analysis: Encoding-Decoding Canonical Segments0
Neural Network Architectures for Arabic Dialect Identification0
Neural Network-Based Modeling of Phonetic Durations0
Neural Network Language Modeling with Letter-based Features and Importance Sampling0
Neural Network Methods for Natural Language Processing by Yoav Goldberg0
Neural Networks Designing Neural Networks: Multi-Objective Hyper-Parameter Optimization0
Hearing-Loss Compensation Using Deep Neural Networks: A Framework and Results From a Listening Test0
Neural Predictive Coding using Convolutional Neural Networks towards Unsupervised Learning of Speaker Characteristics0
Neural Programmer: Inducing Latent Programs with Gradient Descent0
Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition0
Neural Speech Separation Using Spatially Distributed Microphones0
Neural Speech Translation at AppTek0
Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions0
Neural Text Normalization with Subword Units0
Neural Transducer Training: Reduced Memory Consumption with Sample-wise Computation0
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System0
Neuron Activation Profiles for Interpreting Convolutional Speech Recognition Models0
Neuro-oscillatory models of cortical speech processing0
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models0
New Baseline in Automatic Speech Recognition for Northern S\'ami0
New functions for a multipurpose multimodal tool for phonetic and linguistic analysis of very large speech corpora0
Next Wave Artificial Intelligence: Robust, Explainable, Adaptable, Ethical, and Accountable0
Nexus: An Omni-Perceptive And -Interactive Model for Language, Audio, And Vision0
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding0
N-gram Counts and Language Models from the Common Crawl0
N-gram language models for massively parallel devices0
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique0
NN-grams: Unifying neural network and n-gram language models for Speech Recognition0
No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction0
Noise Flooding for Detecting Audio Adversarial Examples Against Automatic Speech Recognition0
Noise in Speech-to-Text Voice: Analysis of Errors and Feasibility of Phonetic Similarity for Their Correction0
Noise Masking Attacks and Defenses for Pretrained Speech Models0
Noise-Robust ASR for the third 'CHiME' Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network0
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge0
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data0
Show:102550
← PrevPage 93 of 129Next →

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