Abbott Po Shun Chen
ABSTRACT
In recent years, artificial intelligence chatbots have attracted more and more attention. The stability and accuracy of automatic speech recognition (ASR) have been improved, making voice more critical in the transaction process and voice consultation of e-commerce purchases. ASR matches the learning model based on contextual cues. Eliminating unnecessary text plays an important role. We use the LSTM model and change it to contextualized custom text. In addition, to use our robot for testing, we propose a multi-task model that can jointly perform content re-scoring and has excellent responsiveness in the text of the input entity. Therefore, this article recommends using ASR technology to interpret and predict the answers to the LSTM model, allowing users to obtain expected results from actual measurements and understand which aspects are suitable for predicting specific emotions tested on this group. This article discusses chatbots and the design techniques used in platform translation, early and modern chatbots combined with ASR and artificial intelligence technology.
- Dharwadkar, Rashmi, and Neeta A. Deshpande. "A medical chatbot." International Journal of Computer Trends and Technology (IJCTT) 2018.Google Scholar
Cross Ref
- Singh, Kamal. "Banks banking on ai." International Journal of Advanced Research in Management and Social Sciences. 2020.Google Scholar
- Følstad, Asbjørn, and Marita Skjuve. "Chatbots for customer service: user experience and motivation." Proceedings of the 1st international conference on conversational user interfaces. 2019.Google Scholar
- Chaudhary, Jitendra, "A Comparative Study of Medical Chatbots. 2021.Google Scholar
- Følstad, Asbjørn, Marita Skjuve, and Petter Bae Brandtzaeg. "Different chatbots for different purposes: towards a typology of chatbots to understand interaction design." International Conference on Internet Science. Springer, Cham, 2018.Google Scholar
- Galitsky, Boris. Developing enterprise chatbots: learning linguistic structures. Springer, 2019.Google Scholar
- Følstad, Asbjørn, Marita Skjuve, and Petter Bae Brandtzaeg. "Different chatbots for different purposes: towards a typology of chatbots to understand interaction design." International Conference on Internet Science. Springer, Cham, 2018.Google Scholar
- Adam, Martin, Michael Wessel, and Alexander Benlian. "AI-based chatbots in customer service and their effects on user compliance." Electronic Markets 31.2 (2021): 427-445.Google ScholarCross Ref
- Larivière, Bart, "Service Encounter 2.0”: An investigation into the roles of technology, employees and customers." Journal of Business Research 79 (2017): 238-246.Google Scholar
- Pantano, Eleonora, and Gabriele Pizzi. "Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis." Journal of Retailing and Consumer Services 55 (2020): 102096.Google ScholarCross Ref
- Huang, Weijiao, Khe Foon Hew, and Donn Emmanuel Gonda. "Designing and evaluating three chatbot-enhanced activities for a flipped graduate course." International Journal of Mechanical Engineering and Robotics Research 8.5 (2019): 6.Google Scholar
- Fogliano, Fernando, "Edgard, the chatbot: questioning ethics in the usage of artificial intelligence through interaction design and electronic literature." International Conference on Human-Computer Interaction. Springer, Cham, 2019.Google Scholar
- Ashktorab, Zahra, "Resilient chatbots: Repair strategy preferences for conversational breakdowns." Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019Google Scholar
- Ding, Shilin, "Using conditional random fields to extract contexts and answers of questions from online forums." Proceedings of ACL-08: HLT. 2008.Google Scholar
- Bahja, M., Hammad, R., Hassouna, M.: Talk2Learn: a framework for chatbot learning. In: Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds.) EC-TEL 2019. LNCS, vol. 11722, pp. 582–586. Springer, Cham (2019).Google Scholar
- Ciechanowski, Leon, "In the shades of the uncanny valley: An experimental study of human–chatbot interaction." Future Generation Computer Systems 92 (2019): 539-548.Google Scholar
- de Haan, Hayco, "Chatbot personality, and customer satisfaction." Info Support Research (2018).Google Scholar
- Crutzen, Rik, "An artificially intelligent chat agent that answers adolescents' questions related to sex, drugs, and alcohol: an exploratory study." Journal of Adolescent Health 48.5 (2011): 514-519.Google ScholarCross Ref
- Ciechanowski, L., Przegalinska, A., Magnuski, M., Gloor, P.: In the shades of the uncanny valley: an experimental study of human–chatbot interaction. Futur. Gener. Comput. Syst. 92, 539–548 (2019)Google ScholarDigital Library
- Ordemann, Stine. “Understanding How Chatbots Work”: An Exploratory Study of Mental Models in Customer Service Chatbots. MS thesis. 2020.Google Scholar
- Ordemann, Stine. “Understanding How Chatbots Work”: An Exploratory Study of Mental Models in Customer Service Chatbots. MS thesis. 2020.Google Scholar
- Antonakaki, D., Spiliotopoulos, D., Samaras, C.V., Pratikakis, P., Ioannidis, S., Fragopoulou, P.: Social media analysis during political turbulence. PLoS One 12 (2017). https://doi.org/10. 1371/journal.pone.0186836Google Scholar
- Shakhovska, Nataliya, Oleh Basystiuk, and Khrystyna Shakhovska. "Development of the Speech-to-Text Chatbot Interface Based on Google API." MoMLeT. 2019Google Scholar
- Burri, Ramón. "Improving user trust towards conversational chatbot interfaces with voice output." (2018).Google Scholar
- Meridian, Erinc, "Dialogue systems for intelligent human-computer interactions." Electronic Notes in Theoretical Computer Science 343 (2019): 57-71.Google Scholar
- Borau, Sylvie, "The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI." Psychology & Marketing 38.7 (2021): 1052-1068.Google ScholarCross Ref
- Liu, Rongjia, and Zhanxun Dong. "A study of user experience in knowledge-based QA chatbot design." International Conference on Intelligent Human Systems Integration. Springer, Cham, 2019.Google Scholar
- Doherty, Dana, and Kevin Curran. "Chatbots for online banking services." Web Intelligence. Vol. 17. No. 4. IOS Press, 2019.Google Scholar
- Kirchhoff, A. S. S. B. K. ASR Adaptation for E-commerce Chatbots using Cross-Utterance Context and Multi-task Language Modeling.Google Scholar
- Miner, Adam S., Liliana Laranjo, and A. Baki Kocaballi. "Chatbots in the fight against the COVID-19 pandemic." NPJ digital medicine 3.1 (2020): 1-4.Google Scholar
- Bharti, Urmil, "Medbot: Conversational artificial intelligence-powered chatbot for delivering telehealth after covid-19." 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020.Google Scholar
- Hussain, Shafquat, Omid Ameri Sianaki, and Nedal Ababneh. "A survey on conversational agents/chatbots classification and design techniques." Workshops of the International Conference on Advanced Information Networking and Applications. Springer, Cham, 2019.Google Scholar
- Rahman, A. M., Abdullah Al Mamun, and Alma Islam. "Programming challenges of chatbot: Current and future prospective." 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2017.Google Scholar
- Waymond, Rodgers. Artificial intelligence in a throughput model: Some major algorithms. CRC Press, 2020.Google Scholar
- Patil, Shruti, "LSTM based Ensemble Network to enhance the learning of long-term dependencies in the chatbot." International Journal for Simulation and Multidisciplinary Design Optimization 11 (2020): 25.Google ScholarCross Ref
- Rothman, Denis. Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills. Packt Publishing Ltd, 2020.Google Scholar
- Jang, Myeongjun, Seungwan Seo, and Pilsung Kang. "Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning." Information Sciences 490 (2019): 59-73.Google Scholar
- Bonetto, Riccardo, "Seq2Seq RNN based gait anomaly detection from smartphone acquired multimodal motion data." arXiv preprint arXiv:1911.08608 (2019).Google Scholar
- Nuruzzaman, Mohammad, and Omar Khadeer Hussain. "A survey on chatbot implementation in the customer service industry through deep neural networks." 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE). IEEE, 2018.Google Scholar
- Lokman, Abbas Salimi, and Mohamed Ariff Aberdeen. "Modern chatbot systems: A technical review." Proceedings of the future technologies conference. Springer, Cham, 2018.Google Scholar
- Gabriel, Raefer, "Further advances in open domain dialog systems in the third Alexa prize socialbot grand challenge." Alexa Prize Proceedings (2020).Google Scholar
- Suhaili, Sinarwati Mohamad, Naomie Salim, and Mohamad Nazim Jambli. "Service chatbots: A systematic review." Expert Systems with Applications (2021): 115461.Google Scholar
- Androutsopoulou, Aggeliki, "Transforming the communication between citizens and government through AI-guided chatbots." Government Information Quarterly 36.2 (2019): 358-367.Google ScholarCross Ref
- Winkler, Rainer, and Matthias Söllner. "Unleashing the potential of chatbots in education: A state-of-the-art analysis." (2018).Google Scholar
- Ren, Yi, "Almost unsupervised text to speech and automatic speech recognition." International Conference on Machine Learning. PMLR, 2019.Google Scholar
- Ruiz, Nicholas, "Assessing the tolerance of neural machine translation systems against speech recognition errors." arXiv preprint arXiv:1904.10997 (2019).Google Scholar
- Liu, Bing. Learning task-oriented dialog with neural network methods. Diss. Carnegie Mellon University, 2018.Google Scholar
- Ischen, Carolin, "I Am Here to Assist You Today”: The Role of Entity, Interactivity and Experiential Perceptions in Chatbot Persuasion." Journal of Broadcasting & Electronic Media 64.4 (2020): 615-639.Google Scholar
- Chen, Abbott Po Shun, "B2B Marketing Crafts Intelligence Commerce: How a Chatbot Is Designed for the Taiwan Agriculture Service." International Journal of e-Education, e-Business, e-Management and e-Learning, 10 (2) (2020): 114-124.Google ScholarCross Ref
- Chen, Abbott Po Shun, "Intelligent Commerce Facilitates Education Technology: The Platform and Chatbot for the Taiwan Agriculture Service."Google Scholar
Index Terms
- Crafting ASR and Conversational Models for an Agriculture Chatbot
Index terms have been assigned to the content through auto-classification.
Recommendations
Accent neutralization for speech recognition of non-native speakers
iiWAS2019: Proceedings of the 21st International Conference on Information Integration and Web-based Applications & ServicesThese days, automatic speech recognition (ASR) systems achieve higher and higher accuracy rates. The score drops significantly, in case when the ASR system is being used with a non-native speaker of the language to be recognized. The main reason is ...
Accent modification for speech recognition of non-native speakers using neural style transfer
AbstractNowadays automatic speech recognition (ASR) systems can achieve higher and higher accuracy rates depending on the methodology applied and datasets used. The rate decreases significantly when the ASR system is being used with a non-native speaker ...
Pitch adaptive MFCC features for improving children's mismatched ASR
A pitch normalization algorithm is proposed for addressing the pitch mismatch between adults' and children's speech for children's automatic speech recognition (ASR). Motivated by the appearance of pitch-dependent distortions in the smoothed mel ...
Comments