Alexa vs. Siri vs. Cortana vs. Google Assistant: A Comparison of Speech-Based Natural User Interfaces

Herrera, Gustavo López; Quirós, Luis Quesada; Blanco, Luis Alberto Guerrero

doi:10.1007/978-3-319-60366-7_23

Cited by 199 publications

(117 citation statements)

References 5 publications

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“…Examples of well-known IPAs include Apple's Siri, Microsoft Cortana, Google Now, Amazon Echo, Samsung S-Voice, IBM's Watson etc. López, Quesada and Guerrero (2017) tested and compared the functionality of Siri, Cortana, Alexa and Google Assistant in eight areas. Testing covered various domains including shopping, entertainment, administration and miscellaneous tasks.…”

Section: Intelligent Personal Assistantsmentioning

confidence: 99%

Overview of Intelligent Personal Assistants

Balcı¹

2019

ACIN

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With the latest technological advances, intelligent personal assistants are becoming part of our daily lives. In the future, with improvements in artificial intelligence techniques, it is foreseen that individuals' life management will be greatly assisted by intelligent personal assistants. Today, it seems that intelligent personal assistants have not yet reached their full potential. For more efficient use, more technical developments are needed, especially in terms of natural language processing. In order to that, areas which need improvement must be determined based on which challenges are faced. In order to develop more efficient solutions for problems, an initial fundamental evaluation is necessary. In this work, features of intelligent personal assistants are examined and existing applications are referred to. For a better understanding of what features an intelligent personal assistant must contain, a literature review was completed. In-use features which should be improved in the future, challenges challenges which need to be overcome, areas which could be made more efficient, and how to increase usage are determined.

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Section: Intelligent Personal Assistantsmentioning

confidence: 99%

Overview of Intelligent Personal Assistants

Balcı¹

2019

ACIN

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“…This assumption makes sense for task-oriented systems such as interactive voice response systems [6] or an information kiosk [7]. However, current spoken dialog systems, such as ''smart speakers'' and ''intelligent personal assistants'' [8], are not only taskoriented voice interfaces but also ''chatbots'' [9]. When used as a non-task-oriented chatbot, the user does not necessarily have a clear goal of the conversation.…”

Section: Introductionmentioning

confidence: 99%

Human–machine metacommunication towards development of a human-like agent: A short review

Ito

2020

Acoust. Sci. & Tech.

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“…As A.I. speakers equipped with Amazon Alexa [4,5], Apple Siri [6][7][8], Google Assistant [9,10] and Microsoft Cortana [11,12] have recently been released and commercialized, it is possible to select music according to user moods [13]. In this way, music-related services such as the recommendation of suitable music will progressively expand by analyzing the emotions of people.…”

Section: Introductionmentioning

confidence: 99%

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Seo

Huh

2019

Electronics

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With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

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Alexa vs. Siri vs. Cortana vs. Google Assistant: A Comparison of Speech-Based Natural User Interfaces

Cited by 199 publications

References 5 publications

Overview of Intelligent Personal Assistants

Overview of Intelligent Personal Assistants

Human–machine metacommunication towards development of a human-like agent: A short review

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

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