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 63016350 of 6433 papers

TitleStatusHype
On-the-Fly Aligned Data Augmentation for Sequence-to-Sequence ASRCode0
Massively Multilingual Neural Grapheme-to-Phoneme ConversionCode0
Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition SystemsCode0
Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech RecognitionCode0
BERT Attends the Conversation: Improving Low-Resource Conversational ASRCode0
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) ZeroCode0
On the Impact of Speech Recognition Errors in Passage Retrieval for Spoken Question AnsweringCode0
FlowSense: Monitoring Airflow in Building Ventilation Systems Using Audio SensingCode0
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case StudyCode0
Regularizing Neural Networks by Penalizing Confident Output DistributionsCode0
Rehearsal-Free Online Continual Learning for Automatic Speech RecognitionCode0
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable ConfidenceCode0
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality ConversionCode0
Stochastic Attention Head Removal: A simple and effective method for improving Transformer Based ASR ModelsCode0
Bandwidth Embeddings for Mixed-bandwidth Speech RecognitionCode0
Direct Segmentation Models for Streaming Speech TranslationCode0
Audio-Linguistic Embeddings for Spoken SentencesCode0
Simple and Effective Zero-shot Cross-lingual Phoneme RecognitionCode0
Measuring the Accuracy of Automatic Speech Recognition SolutionsCode0
Measuring the Contribution of Multiple Model Representations in Detecting Adversarial InstancesCode0
Measuring the Effect of Transcription Noise on Downstream Language Understanding TasksCode0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
Strategies for Training Large Vocabulary Neural Language ModelsCode0
Rene: A Pre-trained Multi-modal Architecture for Auscultation of Respiratory DiseasesCode0
Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language UnderstandingCode0
Transformers: State-of-the-Art Natural Language ProcessingCode0
Audio Adversarial Examples: Targeted Attacks on Speech-to-TextCode0
On the Use of External Data for Spoken Named Entity RecognitionCode0
DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and LocalizationCode0
A Survey on Bayesian Deep LearningCode0
Attentively Embracing Noise for Robust Latent Representation in BERTCode0
FLEURS: Few-shot Learning Evaluation of Universal Representations of SpeechCode0
First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNsCode0
Differentiable Allophone Graphs for Language-Universal Speech RecognitionCode0
Did you hear that? Adversarial Examples Against Automatic Speech RecognitionCode0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
An Adversarial Approach for Explaining the Predictions of Deep Neural NetworksCode0
A Probabilistic Theory of Deep LearningCode0
First Automatic Fongbe Continuous Speech Recognition System: Development of Acoustic Models and Language ModelsCode0
End to End ASR System with Automatic Punctuation InsertionCode0
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition ErrorsCode0
Finnish Parliament ASR corpus - Analysis, benchmarks and statisticsCode0
Streaming End-to-end Speech Recognition For Mobile DevicesCode0
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia ClassificationCode0
A Comparison of Adaptation Techniques and Recurrent Neural Network ArchitecturesCode0
Creating Speech-to-Speech Corpus from Dubbed SeriesCode0
Fine-tuning Strategies for Faster Inference using Speech Self-Supervised Models: A Comparative StudyCode0
Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition SystemsCode0
Coupled Training of Sequence-to-Sequence Models for Accented Speech RecognitionCode0
Residual LSTM: Design of a Deep Recurrent Architecture for Distant Speech RecognitionCode0
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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