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

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Showing 4301–4350 of 6433 papers

TitleStatusHype
MASR: Multi-label Aware Speech Representation—0
Massive End-to-end Models for Short Search Queries—0
Massively Multilingual Adversarial Speech Recognition—0
Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters—0
Massively Multilingual Shallow Fusion with Large Language Models—0
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning—0
MathBridge: A Large Corpus Dataset for Translating Spoken Mathematical Expressions into LaTeX Formulas for Improved Readability—0
Matics Software Suite: New Tools for Evaluation and Data Exploration—0
Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach—0
Maximum a Posteriori Adaptation of Network Parameters in Deep Models—0
May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance—0
May I Ask Who’s Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance—0
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
Mean Field Analysis of Neural Networks: A Central Limit Theorem—0
Measuring Contextual Fitness Using Error Contexts Extracted from the Wikipedia Revision History—0
Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions—0
Measuring Diversified Proficiency of Japanese Learners of English—0
Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition—0
Measuring the Impact of Individual Domain Factors in Self-Supervised Pre-Training—0
Measuring the Influence of Long Range Dependencies with Neural Network Language Models—0
Measuring the Structural Importance through Rhetorical Structure Index—0
MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues—0
MeetDot: Videoconferencing with Live Translation Captions—0
Meet EDGAR, a tutoring agent at MONSERRATE—0
Meeting Transcription Using Virtual Microphone Arrays—0
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems—0
Mel-FullSubNet: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR—0
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition—0
Memory-efficient Speech Recognition on Smart Devices—0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax—0
Memory Visualization for Gated Recurrent Neural Networks in Speech Recognition—0
MERaLiON-AudioLLM: Bridging Audio and Language with Large Language Models—0
Mesures linguistiques automatiques pour l’évaluation des systèmes de Reconnaissance Automatique de la Parole (Automated linguistic measures for automatic speech recognition systems’ evaluation)—0
Meta Auxiliary Learning for Low-resource Spoken Language Understanding—0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
Meta-Gating Framework for Fast and Continuous Resource Optimization in Dynamic Wireless Environments—0
Meta Learning for End-to-End Low-Resource Speech Recognition—0
Meta-Learning for improving rare word recognition in end-to-end ASR—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
Methods to Increase the Amount of Data for Speech Recognition for Low Resource Languages—0
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction—0
MFLA: Monotonic Finite Look-ahead Attention for Streaming Speech Recognition—0
面向 Transformer 模型的蒙古语语音识别词特征编码方法(Researching of the Mongolian word encoding method based on Transformer Mongolian speech recognition)—0
Microphone Array Geometry Independent Multi-Talker Distant ASR: NTT System for the DASR Task of the CHiME-8 Challenge—0
Microsoft Speech Language Translation (MSLT) Corpus: The IWSLT 2016 release for English, French and German—0
Mi-Go: Test Framework which uses YouTube as Data Source for Evaluating Speech Recognition Models like OpenAI's Whisper—0
MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation—0
MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition—0
Minimal Feature Analysis for Isolated Digit Recognition for varying encoding rates in noisy environments—0
Minimally-Supervised Morphological Segmentation using Adaptor Grammars—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

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 BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage 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
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word 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