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 33013350 of 6433 papers

TitleStatusHype
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition0
Multiword Expressions and the Low-Resource Scenario from the Perspective of a Local Oral Culture0
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling0
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition0
MuST-C: a Multilingual Speech Translation Corpus0
Mutually-Constrained Monotonic Multihead Attention for Online ASR0
MVA: The Multimodal Virtual Assistant0
My Science Tutor---Learning Science with a Conversational Virtual Tutor0
My Science Tutor (MyST) -- A Large Corpus of Children's Conversational Speech0
NAS-Bench-ASR: Reproducible Neural Architecture Search for Speech Recognition0
NAS-Bench-Suite: NAS Evaluation is (Now) Surprisingly Easy0
NaturalL2S: End-to-End High-quality Multispeaker Lip-to-Speech Synthesis with Differential Digital Signal Processing0
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances0
N-best T5: Robust ASR Error Correction using Multiple Input Hypotheses and Constrained Decoding Space0
NCSU\_SAS\_WOOKHEE: A Deep Contextual Long-Short Term Memory Model for Text Normalization0
Nearly Zero-Shot Learning for Semantic Decoding in Spoken Dialogue Systems0
NeMo: a toolkit for building AI applications using Neural Modules0
Nepali Speech Recognition Using CNN, GRU and CTC0
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training0
NEURAghe: Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs0
Neural approaches to spoken content embedding0
Neural Architecture Search For LF-MMI Trained Time Delay Neural Networks0
Neural Architecture Search with an Efficient Multiobjective Evolutionary Framework0
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection0
Neural Blind Source Separation and Diarization for Distant Speech Recognition0
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
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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