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== Key Innovations == ==== Significant Shift : Simplifying Speech Recognition ==== End-to-end modeling can directly translate the speech input into the output only using a single neural network, unlike the traditional one, which has several independent elements. In traditional ASR, the majority of ASR systems comprise distinct acoustic, pronunciation, and language model components, each trained separately. The creation of a pronunciation lexicon and the specification of phoneme sets for a specific language necessitate expertise and are time-intensive tasks. ([https://ibb.co/SQ4x7fF]) shows its structure. [https://ibb.co/5FGLdfG E2E] (Click here to view the end-to-end models workflow) speech recognition significantly simplifies the complexity of traditional models. Manual labeling of information is unnecessary, as the neural network can autonomously learn language and pronunciation details.<ref>Wang, Dong, Xiaodong Wang, and Shaohe Lv. 2019. βAn Overview of End-to-End Automatic Speech Recognition.β ''Symmetry'' 11(8):1018.https://www.mdpi.com/2073-8994/11/8/1018</ref> ==== Main Structures of End-to-End models ==== ===== 1. Connectionist Temporal Classification (CTC) ===== As a technique to train an acoustic model without the need for precise frame-level alignments. Initially, using CTC to generate target phonemes didn't truly constitute an end-to-end method, as it still relied on language models. CTC allows for training an acoustic model without the necessity of frame-level alignments aligning acoustic data with the transcriptions. The utilization of CTC as the loss function in training the acoustic model represents an end-to-end training approach. This method eliminates the necessity for prior data alignment, requiring only an input sequence and an output sequence for training. Consequently, manual alignment and labeling of data become unnecessary. Moreover, CTC's direct output sequence prediction doesn't require external post-processing. Within the CTC framework, 'blank' is introduced (denoting no predicted value for a given frame). Each prediction's classification corresponds to a spike in the speech waveform, while non-spike regions are considered 'blank.' In a speech sequence, CTC ultimately generates a spike sequence, irrespective of each phoneme's duration. ===== 2. Recurrent Neural Network (RNN)-Transducer ===== RNN-transducer lists all potential rigid alignments and aggregates them for achieving flexible alignment. However, unlike CTC, RNN-transducer does not assume label independence during the enumeration of rigid alignments. Consequently, it differs from CTC in terms of how paths are defined and probabilities are calculated.<ref>Boyer, Florian, Yusuke Shinohara, Takaaki Ishii, Hirofumi Inaguma, and Shinji Watanabe. βA Study of Transducer Based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies.β arXiv, January 14, 2022. <nowiki>http://arxiv.org/abs/2201.05420</nowiki>.</ref> ===== 3. Attention Mechanism ===== Attention-based Encoder-Decoder Models made their initial appearance within the domain of neural machine translation. The primary purpose of the Attention Mechanism is to address issues present in traditional RNN-based sequence-to-sequence models. This approach takes a different route by foregoing the enumeration of all potential rigid alignments. Instead, it employs the Attention mechanism to directly get soft alignment details between the input data and output labels. ==== Advantage of End-to-End Models ASR ==== Initially, end-to-end models simplify the ASR pipeline substantially by directly generating characters or even words. Conversely, the design of traditional hybrid models is intricate, demanding extensive expertise and years of ASR experience. Furthermore, the utilization of a single network for ASR makes end-to-end models significantly more compact compared to traditional hybrid models. This compactness enables easy deployment of end-to-end models on high-accuracy devices.<ref>Li J .Recent Advances in End-to-End Automatic Speech Recognition[J]. 2021.https://arxiv.org/abs/2111.01690</ref> Last, end-to-end models has a much simpler training approach with models, which reduces learning time, decoding time, and allows joint optimization with subsequent processing, such as understanding the natural language.<ref>Orken M , Dina O , Keylan A ,et al.A study of transformer-based end-to-end speech recognition system for Kazakh language[J].Scientific Reports[2023-09-17].https://www.nature.com/articles/s41598-022-12260-y</ref>
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