C5W3L09 SpeechRecog

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C5W3L09 SpeechRecog 0:08:54

C5W3L09 SpeechRecog

Connectionist Temporal Classification 0:20:23

Connectionist Temporal Classification (CTC) Explained

[Olewave's Long Review] 0:38:31

[Olewave's Long Review] Efficient Training of Neural Transducer for Speech Recognition

C5W3L10 Trigger Word 0:05:03

C5W3L10 Trigger Word Detection

C4W3L08 Anchor Boxes 0:09:43

C4W3L08 Anchor Boxes

C5W3L11 Summary and 0:02:45

C5W3L11 Summary and Thank You

Connectionist Temporal Classification: 1:35:19

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Connectionist Temporal Classification 0:14:01

Connectionist Temporal Classification (CTC) @Automatic speech recognition

CSC649 Individual Project. 0:07:21

CSC649 Individual Project. Deep Learning : Speech Recognition

09: RNN-T ASR 0:11:35

09: RNN-T ASR Biasing For Contextual words

Saliency Tubes: Visual 0:01:01

Saliency Tubes: Visual Explanations for Spatio Temporal Convolutions

Connectionist Temporal Classification 0:21:12

Connectionist Temporal Classification | Lecture 72 (Part 2) | Applied Deep Learning

Wav2vec2 A Framework 0:14:44

Wav2vec2 A Framework for Self-Supervised Learning of Speech Representations - Paper Explained

running nn.CTCLoss (1) 0:00:35

running nn.CTCLoss (1)

Coming soon: a 0:00:32

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CTC Beam Search 0:20:18

CTC Beam Search | Lecture 74 (Part 1) | Applied Deep Learning

ML4Audio - pyctcdecode: 0:57:26

ML4Audio - pyctcdecode: A simple and fast speech-to-text prediction decoding algorithm

Supervised Adaptation of 0:26:48

Supervised Adaptation of Sequence-to-Sequence Speech Recognition Systems using Batch-Weighting

Pyctcdecode & Speech2text 0:31:19

Pyctcdecode & Speech2text decoding

CRNN: A Joint 0:01:01

CRNN: A Joint Neural Network for Redundancy Detection

C5W3L06 Bleu Score 0:16:26

C5W3L06 Bleu Score (Optional)

Hierarchical multi-task learning 0:23:08

Hierarchical multi-task learning for ctc based speech recognition

C5W3L02 Picking the 0:08:57

C5W3L02 Picking the most likely sentence

Part1-1: speech recognition 0:20:04

Part1-1: speech recognition

Комментарии
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In my opinion, the dnn-hmm hybrid approach is still competitive. Not Sure, but Microsofts human level paper used a ensemble with all Kind of models. IBM did something very similiar. Only baidu relies on ctc only?? Or do all companies Switch to attention seq2seq??

randywelt
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Where is C5W4 lectures . Its not working on Coursera

ambrishgupta
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sppech recognition models .little bit tough

sandipansarkar