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A Comparative Analysis of Generative Neural attention-based Service Chatbot

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 742-751页

作者： Suhaili, Sinarwati Mohamad Salim, Naomie Jambli, Mohamad Nazim Pre Univ Kota Samarahan Sarawak Malaysia Univ Teknol Malaysia Fac Comp Skudai 81310 Johor Malaysia Univ Teknol Malaysia Ibnu Sina Inst Sci & Ind Res UTM Big Data Ctr Skudai 81310 Johor Malaysia Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan Sarawak Malaysia

Companies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter. In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices. The primary focus is to automate the generation of conversational chat between a computer and a human by constructing vir-tual service agents that can predict appropriate and automatic responses to customers' queries. This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets. The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The RNNs are augmented with an attention layer to focus on important information between input and output sequences. Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model. Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experi-ment. Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model's performance. Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText. Thus, the finding significantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model significantly outperformed baseline approaches in their BLEU score as a promising use in future works.

关键词： Sequence-to-sequence encoder-decoder service chatbot attention-based encoder-decoder Recurrent Neural Network (RNN) Long Short-Term Memory (LSTM) Gated Recurrent Unit (GRU) word embedding

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On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition 21

On the Comparison of Popular End-to-End Models for Large Sca...

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Interspeech Conference

作者： Li, Jinyu Wu, Yu Gaur, Yashesh Wang, Chengyi Zhao, Rui Liu, Shujie Microsoft Speech & Language Grp Redmond WA 98052 USA Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781713820697

Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E) models for automatic speech recognition. Currently, there are three promising E2E methods: recurrent neural network transducer (RNN-T), RNN attention-based encoder-decoder (AED), and Transformer-AED. In this study, we conduct an empirical comparison of RNN-T, RNN-AED, and Transformer-AED models, in both non-streaming and streaming modes. We use 65 thousand hours of Microsoft anonymized training data to train these models. As E2E models are more data hungry, it is better to compare their effectiveness with large amount of training data. To the best of our knowledge, no such comprehensive study has been conducted yet. We show that although AED models are stronger than RNN-T in the non-streaming mode, RNN-T is very competitive in streaming mode if its encoder can be properly initialized. Among all three E2E models, transformer-AED achieved the best accuracy in both streaming and non-streaming mode. We show that both streaming RNN-T and transformer-AED models can obtain better accuracy than a highly-optimized hybrid model.

关键词： end-to-end RNN-transducer attention-based encoder-decoder transformer

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Serialized Output Training for End-to-End Overlapped Speech Recognition 21

Serialized Output Training for End-to-End Overlapped Speech ...

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Interspeech Conference

作者： Kanda, Naoyuki Gaur, Yashesh Wang, Xiaofei Meng, Zhong Yoshioka, Takuya Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781713820697

This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant training (PIT), SOT uses a model with only one output layer that generates the transcriptions of multiple speakers one after another. The attention and decoder modules take care of producing multiple transcriptions from overlapped speech. SOT has two advantages over PIT: (1) no limitation in the maximum number of speakers, and (2) an ability to model the dependencies among outputs for different speakers. We also propose a simple trick that allows SOT to be executed in O(S), where S is the number of the speakers in the training sample, by using the start times of the constituent source utterances. Experimental results on LibriSpeech corpus show that the SOT models can transcribe overlapped speech with variable numbers of speakers significantly better than PIT-based models. We also show that the SOT models can accurately count the number of speakers in the input audio.

关键词： multi-speaker speech recognition attention-based encoder-decoder permutation invariant training serialized output training

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A Human-Inspired Recognition System for Pre-Modern Japanese Historical Documents

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IEEE ACCESS 2019年 7卷 84163-84169页

作者： Ann Duc Le Clanuwat, Tarin Kitamoto, Asanobu Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ctr Open Data Humanities Tokyo 1018430 Japan

Recognition of historical documents is a challenging problem due to the noised, damaged characters, and background. However, in Japanese historical documents, not only contains the mentioned problems, pre-modern Japanese characters were written in cursive and are connected. Therefore, character segmentation-based methods do not work well. This leads to the idea of creating a new recognition system. In this paper, we propose a human-inspired document reading system to recognize multiple lines of pre-modern Japanese historical documents. During the reading, people employ eyes movement to determine the start of a text line. Then, they move the eyes from the current character/word to the next character/word. They can also determine the end of a line or skip a figure to move to the next line. The eyes movement integrates with visual processing to operate the reading process in the brain. We employ attention-based encoder-decoder to implement this recognition system. First, the recognition system detects were to start a text line. Second, the system scans and recognize character by character until the text line is completed. Then, the system continues to detect the start of the next text line. This process is repeated until reading the whole document. As results, the system is successful to recognize multiple lines, connected and cursive characters without performing character/line segmentation. Besides, we also employ a coverage model which stores the history of eyes movement to predict the next movement more precisely. We tested our human-inspired recognition system on the pre-modern Japanese historical document provided by the PRMU Kuzushiji competition. The results of the experiments demonstrate the superiority and effectiveness of our proposed system by achieving Sequence Error Rate of 9.87% and 53.81% on level 2 and level 3 of the dataset, respectively. These results outperform to any other systems participated in the PRMU Kuzushiji competition.

关键词： A human reading-inspired recognition system recognition of pre-modern Japanese historical document attention-based encoder-decoder Kuzushiji

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Deep Learning Approach for Receipt Recognition 6th

Deep Learning Approach for Receipt Recognition

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6th International Conference on Future Data and Security Engineering (FDSE)

作者： Anh Duc Le Dung Van Pham Tuan Anh Nguyen Res Org Informat & Syst Ctr Open Data Humanities Tokyo Japan Deep Learning & Applicat Ho Chi Minh City Vietnam

ISBN: (纸本)9783030356538;9783030356521

Inspired by the recent successes of deep learning on Computer Vision and Natural Language Processing, we present a deep learning approach for recognizing scanned receipts. The recognition system has two main modules: text detection based on Connectionist Text Proposal Network and text recognition based on attention-based encoder-decoder. We also proposed preprocessing to extract receipt area and OCR verification to ignore handwriting. The experiments on the dataset of the Robust Reading Challenge on Scanned Receipts OCR and Information Extraction 2019 demonstrate that the accuracies were improved by integrating the pre-processing and the OCR verification. Our recognition system achieved 71.9% of the F1 score for detection and recognition task.

关键词： Receipt recognition Connectionist Text Proposal Network attention-based encoder-decoder

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SPEAKER ADAPTATION FOR MULTICHANNEL END-TO-END SPEECH RECOGNITION

SPEAKER ADAPTATION FOR MULTICHANNEL END-TO-END SPEECH RECOGN...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Ochiai, Tsubasa Watanabe, Shinji Katagiri, Shigeru Hori, Takaaki Hershey, John Doshisha Univ Grad Sch Sci & Engn Kyoto Japan Mitsubishi Elect Res Labs Cambridge MA USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781538646588

Recent work on multichannel end-to-end automatic speech recognition (ASR) has shown that multichannel speech enhancement and speech recognition functions can be integrated into a deep neural network (DNN)-based system, and promising experimental results have been shown using the CHiME-4 and AMI corpora. In other recent DNN-based hidden Markov model (DNN-HMM) hybrid architectures, the effectiveness of speaker adaptation has been well established. Motivated by these results, we propose a multi-path adaptation scheme for end-to-end multichannel ASR, which combines the unprocessed noisy speech features with a speech-enhanced pathway to improve upon previous end-to-end ASR approaches. Experimental results using CHiME-4 show that (1) our proposed multi-path adaptation scheme improves ASR performance and (2) adapting the encoder network is more effective than adapting the neural beam-former, attention mechanism, or decoder network.

关键词： multichannel end-to-end ASR neural beam-former attention-based encoder-decoder speaker adaptation

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ON MODULAR TRAINING OF NEURAL ACOUSTICS-TO-WORD MODEL FOR LVCSR

ON MODULAR TRAINING OF NEURAL ACOUSTICS-TO-WORD MODEL FOR LV...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Chen, Zhehuai Liu, Qi Li, Hao Yu, Kai Shanghai Jiao Tong Univ Dept Comp Sci & Engn SpeechLab Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai Peoples R China

ISBN: (纸本)9781538646588

End-to-end (E2E) automatic speech recognition (ASR) systems directly map acoustics to words using a unified model. Previous works mostly focus on E2E training a single model which integrates acoustic and language model into a whole. Although E2E training benefits from sequence modeling and simplified decoding pipelines, large amount of transcribed acoustic data is usually required, and traditional acoustic and language modelling techniques cannot be utilized. In this paper, a novel modular training framework of E2E ASR is proposed to separately train neural acoustic and language models during training stage, while still performing end-to-end inference in decoding stage. Here, an acoustics-to-phoneme model (A2P) and a phoneme-to-word model (P2W) are trained using acoustic data and text data respectively. A phone synchronous decoding (PSD) module is inserted between A2P and P2W to reduce sequence lengths without precision loss. Finally, modules are integrated into an acoustics-to-word model (A2W) and jointly optimized using acoustic data to retain the advantage of sequence modeling. Experiments on a 300-hour Switchboard task show significant improvement over the direct A2W model. The efficiency in both training and decoding also benefits from the proposed method.

关键词： E2E Connectionist Temporal Classification attention-based encoder-decoder Phone Synchronous Decoding

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IMPROVING OOV DETECTION AND RESOLUTION WITH EXTERNAL LANGUAGE MODELS IN ACOUSTIC-TO-WORD ASR

IMPROVING OOV DETECTION AND RESOLUTION WITH EXTERNAL LANGUAG...

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IEEE Workshop on Spoken Language Technology (SLT)

作者： Inaguma, Hirofumi Mimura, Masato Sakai, Shinsuke Kawahara, Tatsuya Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9781538643341

Acoustic-to-word (A2W) end-to-end automatic speech recognition (ASR) systems have attracted attention because of an extremely simpli fi ed architecture and fast decoding. To alleviate data sparseness issues due to infrequent words, the combination with an acoustic-to-character (A2C) model is investigated. Moreover, the A2C model can be used to recover out-of-vocabulary (OOV) words that are not covered by the A2W model, but this requires accurate detection of OOV words. A2W models learn contexts with both acoustic and transcripts;therefore they tend to falsely recognize OOV words as words in the vocabulary. In this paper, we tackle this problem by using external language models (LM), which are trained only with transcriptions and have better linguistic information to detect OOV words. The A2C model is used to resolve these OOV words. Experimental evaluations show that external LMs have the effects of not only reducing errors but also increasing the number of detected OOV words, and the proposed method significantly improves performances in English conversational and Japanese lecture corpora, especially for out-of-domain scenario. We also investigate the impact of the vocabulary size of A2W models and the data size for training LMs. Moreover, our approach can reduce the vocabulary size several times with marginal performance degradation.

关键词： End-to-end speech recognition acoustic-to-word attention-based encoder-decoder multi-task learning recurrent neural network language model OOV

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Speaker Adaptation for Multichannel End-to-End Speech Recognition

Speaker Adaptation for Multichannel End-to-End Speech Recogn...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Tsubasa Ochiai Shinji Watanabe Shigeru Katagiri Takaaki Hori John Hershey Graduate School of Science and Engineering Doshisha University Kyoto Japan Mitsubishi Electric Research Laboratories Massachusetts USA

ISBN: (纸本)9781538646595

关键词： multichannel end-to-end ASR neural beamformer attention-based encoder-decoder speaker adaptation

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ON MODULAR TRAINING OF NEURAL ACOUSTICS-TO-WORD MODEL FOR LVCSR

ON MODULAR TRAINING OF NEURAL ACOUSTICS-TO-WORD MODEL FOR LV...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhehuai Chen Qi Liu Hao Li Kai Yu Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering SpeechLab Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781538646595

End-to-end (E2E) automatic speech recognition (ASR) systems directly map acoustics to words using a unified model. Previous works mostly focus on E2E training a single model which integrates a-coustic and language model into a whole. Although E2E training benefits from sequence modeling and simplified decoding pipelines, large amount of transcribed acoustic data is usually required, and traditional acoustic and language modelling techniques cannot be utilized. In this paper, a novel modular training framework of E2E ASR is proposed to separately train neural acoustic and language models during training stage, while still performing end-to-end inference in decoding stage. Here, an acoustics-to-phoneme model (A2P) and a phoneme-to-word model (P2W) are trained using acoustic data and text data respectively. A phone synchronous decoding (PSD) module is inserted between A2P and P2W to reduce sequence lengths without precision loss. Finally, modules are integrated into an acoustics-to-word model (A2W) and jointly optimized using acoustic data to retain the advantage of sequence modeling. Experiments on a 300-hour Switchboard task show significant improvement over the direct A2W model. The efficiency in both training and decoding also benefits from the proposed method.

关键词： E2E Connectionist Temporal Classification attention-based encoder-decoder Phone Synchronous Decoding end-to-end Acoustic data modelling languages DECODING Acoustics Speech recognition Training

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