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 20012050 of 6433 papers

TitleStatusHype
Cross-Lingual Word Embeddings for Low-Resource Language Modeling0
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition0
Attention-Based End-to-End Speech Recognition on Voice Search0
Cross-Lingual Transfer Learning for Speech Translation0
E-Branchformer: Branchformer with Enhanced merging for speech recognition0
Echo State Speech Recognition0
Alignment Knowledge Distillation for Online Streaming Attention-based Speech Recognition0
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications0
Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devices0
EdgeCRNN: an edgecomputing oriented model of acoustic feature enhancement for keyword spotting0
A comparable study of modeling units for end-to-end Mandarin speech recognition0
EdgeSpeechNets: Highly Efficient Deep Neural Networks for Speech Recognition on the Edge0
探討聲學模型的合併技術與半監督鑑別式訓練於會議語音辨識之研究 (Investigating acoustic model combination and semi-supervised discriminative training for meeting speech recognition) [In Chinese]0
會議語音辨識使用語者資訊之語言模型調適技術 (On the Use of Speaker-Aware Language Model Adaptation Techniques for Meeting Speech Recognition ) [In Chinese]0
EEG based Continuous Speech Recognition using Transformers0
使用長短期記憶類神經網路建構中文語音辨識器之研究 (A study on Mandarin speech recognition using Long Short-Term Memory neural network) [In Chinese]0
Effective Decoder Masking for Transformer Based End-to-End Speech Recognition0
Effectively pretraining a speech translation decoder with Machine Translation data0
Effectiveness of Mining Audio and Text Pairs from Public Data for Improving ASR Systems for Low-Resource Languages0
Cross-lingual Synonymy Overlap0
Cross-lingual studies of ASR errors: paradigms for perceptual evaluations0
Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition0
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition0
Effective Text Adaptation for LLM-based ASR through Soft Prompt Fine-Tuning0
Cross-lingual projection for class-based language models0
Effect of noise suppression losses on speech distortion and ASR performance0
Attention-based ASR with Lightweight and Dynamic Convolutions0
Alignment-Free Training for Transducer-based Multi-Talker ASR0
Effects of Number of Filters of Convolutional Layers on Speech Recognition Model Accuracy0
Effects of Speaker Count, Duration, and Accent Diversity on Zero-Shot Accent Robustness in Low-Resource ASR0
Cross-Lingual Machine Speech Chain for Javanese, Sundanese, Balinese, and Bataks Speech Recognition and Synthesis0
Efficient acoustic feature transformation in mismatched environments using a Guided-GAN0
Boosting Active Learning for Speech Recognition with Noisy Pseudo-labeled Samples0
Cross-Lingual Language Modeling with Syntactic Reordering for Low-Resource Speech Recognition0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers0
Cross-lingual Embedding Clustering for Hierarchical Softmax in Low-Resource Multilingual Speech Recognition0
Efficient Compression of Multitask Multilingual Speech Models0
Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systems0
Efficient data selection employing Semantic Similarity-based Graph Structures for model training0
Alignment Entropy Regularization0
Efficient Disfluency Detection with Transition-based Parsing0
Efficient Domain Adaptation for Speech Foundation Models0
Prompt-tuning in ASR systems for efficient domain-adaptation0
Efficient Dynamic WFST Decoding for Personalized Language Models0
A Curriculum Learning Method for Improved Noise Robustness in Automatic Speech Recognition0
Cross-Lingual Conversational Speech Summarization with Large Language Models0
Crossing the SSH Bridge with Interview Data0
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR0
A Treatise On FST Lattice Based MMI Training0
Alignment-Based Neural Machine Translation0
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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