Implementation of Voice User Interface Using Speech Recognition
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Voice Interfaces
VIEW POINT VOICE INTERFACES Abstract A voice-user interface (VUI) makes human interaction with computers possible through a voice/speech platform in order to initiate an automated service or process. This Point of View explores the reasons behind the rise of voice interface, key challenges enterprises face in voice interface adoption and the solution to these. Are We Ready for Voice Interfaces? Let’s get talking! IO showed the new promise of voice offering integrations with their Voice interfaces. Assistants. Since Apple integration with Siri, voice interfaces has significantly Almost all the big players (Google, Apple, As per industry forecasts, over the next progressed. Echo and Google Home Microsoft) have ‘office productivity’ decade, 8 out of every 10 people in the have demonstrated that we do not need applications that are being adopted by world will own a device (a smartphone or a user interface to talk to computers businesses (Microsoft and their Office some kind of assistant) which will support and have opened-up a new channel for Suite already have a big advantage here, voice based conversations in native communication. Recent demos of voice but things like Google Docs and Keynote language. Just imagine the impact! based personal assistance at Google are sneaking in), they have also started Voice Assistant Market USD~7.8 Billion CAGR ~39% Market Size (USD Billion) 2016 2017 2018 2019 2020 2021 2022 2023 The Sudden Interest in Voice Interfaces Although voice technology/assistants Voice Recognition Accuracy Convenience – speaking vs typing have been around in some shape or form Voice Recognition accuracy continues to Humans can speak 150 words per minute for many years, the relentless growth of improve as we now have the capability to vs the typing speed of 40 words per low-cost computational power—and train the models using neural networks minute. -
The Role of Higher-Level Linguistic Features in HMM-Based Speech Synthesis
INTERSPEECH 2010 The role of higher-level linguistic features in HMM-based speech synthesis Oliver Watts, Junichi Yamagishi, Simon King Centre for Speech Technology Research, University of Edinburgh, UK [email protected] [email protected] [email protected] Abstract the annotation of a synthesiser’s training data on listeners’ re- action to the speech produced by that synthesiser is still unclear We analyse the contribution of higher-level elements of the lin- to us. In particular, we suspect that features such as pitch ac- guistic specification of a data-driven speech synthesiser to the cent type which might be useful in voice-building if labelled naturalness of the synthetic speech which it generates. The reliably, are under-used in conventional systems because of la- system is trained using various subsets of the full feature-set, belling/prediction errors. For building the systems to be evalu- in which features relating to syntactic category, intonational ated we therefore used data for which hand labelled annotation phrase boundary, pitch accent and boundary tones are selec- of ToBI events is available. This allows us to compare ideal sys- tively removed. Utterances synthesised by the different config- tems built from a corpus where these higher-level features are urations of the system are then compared in a subjective evalu- accurately annotated with more conventional systems that rely ation of their naturalness. exclusively on prediction of these features from text for annota- The work presented forms background analysis for an on- tion of their training data. going set of experiments in performing text-to-speech (TTS) conversion based on shallow features: features that can be triv- 2. -
NLP-5X Product Brief
NLP-5x Natural Language Processor With Motor, Sensor and Display Control The NLP-5x is Sensory’s new Natural Language Processor targeting consumer electronics. The NLP-5x is designed to offer advanced speech recognition, text-to-speech (TTS), high quality music and speech synthesis and robotic control to cost-sensitive high volume products. Based on a 16-bit DSP, the NLP-5x integrates digital and analog processing blocks and a wide variety of communication interfaces into a single chip solution minimizing the need for external components. The NLP-5x operates in tandem with FluentChip™ firmware - an ultra- compact suite of technologies that enables products with up to 750 seconds of compressed speech, natural language interface grammars, TTS synthesis, Truly Hands-Free™ triggers, multiple speaker dependent and independent vocabularies, high quality stereo music, speaker verification (voice password), robotics firmware and all application code built into the NLP-5x as a single chip solution. The NLP-5x also represents unprecedented flexibility in application hardware designs. Thanks to the highly integrated architecture, the most cost-effective voice user interface (VUI) designs can be built with as few additional parts as a clock crystal, speaker, microphone, and few resistors and capacitors. The same integration provides all the necessary control functions on-chip to enable cost-effective man-machine interfaces (MMI) with sensing technologies, and complex robotic products with motors, displays and interactive intelligence. Features BROAD -
The RACAI Text-To-Speech Synthesis System
The RACAI Text-to-Speech Synthesis System Tiberiu Boroș, Radu Ion, Ștefan Daniel Dumitrescu Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy (RACAI) [email protected], [email protected], [email protected] of standalone Natural Language Processing (NLP) Tools Abstract aimed at enabling text-to-speech (TTS) synthesis for less- This paper describes the RACAI Text-to-Speech (TTS) entry resourced languages. A good example is the case of for the Blizzard Challenge 2013. The development of the Romanian, a language which poses a lot of challenges for TTS RACAI TTS started during the Metanet4U project and the synthesis mainly because of its rich morphology and its system is currently part of the METASHARE platform. This reduced support in terms of freely available resources. Initially paper describes the work carried out for preparing the RACAI all the tools were standalone, but their design allowed their entry during the Blizzard Challenge 2013 and provides a integration into a single package and by adding a unit selection detailed description of our system and future development speech synthesis module based on the Pitch Synchronous directions. Overlap-Add (PSOLA) algorithm we were able to create a fully independent text-to-speech synthesis system that we refer Index Terms: speech synthesis, unit selection, concatenative to as RACAI TTS. 1. Introduction A considerable progress has been made since the start of the project, but our system still requires development and Text-to-speech (TTS) synthesis is a complex process that although considered premature, our participation in the addresses the task of converting arbitrary text into voice. -
Synthesis and Recognition of Speech Creating and Listening to Speech
ISSN 1883-1974 (Print) ISSN 1884-0787 (Online) National Institute of Informatics News NII Interview 51 A Combination of Speech Synthesis and Speech Oct. 2014 Recognition Creates an Affluent Society NII Special 1 “Statistical Speech Synthesis” Technology with a Rapidly Growing Application Area NII Special 2 Finding Practical Application for Speech Recognition Feature Synthesis and Recognition of Speech Creating and Listening to Speech A digital book version of “NII Today” is now available. http://www.nii.ac.jp/about/publication/today/ This English language edition NII Today corresponds to No. 65 of the Japanese edition [Advance Notice] Great news! NII Interview Yamagishi-sensei will create my voice! Yamagishi One result is a speech translation sys- sound. Bit (NII Character) A Combination of Speech Synthesis tem. This system recognizes speech and translates it Ohkawara I would like your comments on the fu- using machine translation to synthesize speech, also ture challenges. and Speech Recognition automatically translating it into every language to Ono The challenge for speech recognition is how speak. Moreover, the speech is created with a human close it will come to humans in the distant speech A Word from the Interviewer voice. In second language learning, you can under- case. If this study is advanced, it will be possible to Creates an Affluent Society stand how you should pronounce it with your own summarize the contents of a meeting and to automati- voice. If this is further advanced, the system could cally take the minutes. If a robot understands the con- have an actor in a movie speak in a different language tents of conversations by multiple people in a natural More and more people have begun to use smart- a smartphone, I use speech input more often. -
A Guide to Chatbot Terminology
Machine Language A GUIDE TO CHATBOT TERMINOLOGY Decision Trees The most basic chatbots are based on tree-structured flowcharts. Their responses follow IF/THEN scripts that are linked to keywords and buttons. Natural Language Processing (NLP) A computer’s ability to detect human speech, recognize patterns in conversation, and turn text into speech. Natural Language Understanding A computer’s ability to determine intent, especially when what is said doesn’t quite match what is meant. This task is much more dicult for computers than Natural Language Processing. Layered Communication Human communication is complex. Consider the intricacies of: • Misused phrases • Intonation • Double meanings • Passive aggression • Poor pronunciation • Regional dialects • Subtle humor • Speech impairments • Non-native speakers • Slang • Syntax Messenger Chatbots Messenger chatbots reside within the messaging applications of larger digital platforms (e.g., Facebook, WhatsApp, Twitter, etc.) and allow businesses to interact with customers on the channels where they spend the most time. Chatbot Design Programs There’s no reason to design a messenger bot from scratch. Chatbot design programs help designers make bots that: • Can be used on multiple channels • (social, web, apps) • Have custom design elements • (response time, contact buttons, • images, audio, etc.) • Collect payments • Track analytics (open rates, user • retention, subscribe/unsubscribe • rates) • Allow for human takeover when • the bot’s capabilities are • surpassed • Integrate with popular digital • platforms (Shopify, Zapier, Google • Site Search, etc.) • Provide customer support when • issues arise Voice User Interface A voice user interface (VUI) allows people to interact with a computer through spoken commands and questions. Conversational User Interface Like a voice user interface, a conversational user interface (CUI) allows people to control a computer with speech, but CUI’s dier in that they emulate the nuances of human conversation. -
The Role of Speech Processing in Human-Computer Intelligent Communication
THE ROLE OF SPEECH PROCESSING IN HUMAN-COMPUTER INTELLIGENT COMMUNICATION Candace Kamm, Marilyn Walker and Lawrence Rabiner Speech and Image Processing Services Research Laboratory AT&T Labs-Research, Florham Park, NJ 07932 Abstract: We are currently in the midst of a revolution in communications that promises to provide ubiquitous access to multimedia communication services. In order to succeed, this revolution demands seamless, easy-to-use, high quality interfaces to support broadband communication between people and machines. In this paper we argue that spoken language interfaces (SLIs) are essential to making this vision a reality. We discuss potential applications of SLIs, the technologies underlying them, the principles we have developed for designing them, and key areas for future research in both spoken language processing and human computer interfaces. 1. Introduction Around the turn of the twentieth century, it became clear to key people in the Bell System that the concept of Universal Service was rapidly becoming technologically feasible, i.e., the dream of automatically connecting any telephone user to any other telephone user, without the need for operator assistance, became the vision for the future of telecommunications. Of course a number of very hard technical problems had to be solved before the vision could become reality, but by the end of 1915 the first automatic transcontinental telephone call was successfully completed, and within a very few years the dream of Universal Service became a reality in the United States. We are now in the midst of another revolution in communications, one which holds the promise of providing ubiquitous service in multimedia communications. -
Voice User Interface on the Web Human Computer Interaction Fulvio Corno, Luigi De Russis Academic Year 2019/2020 How to Create a VUI on the Web?
Voice User Interface On The Web Human Computer Interaction Fulvio Corno, Luigi De Russis Academic Year 2019/2020 How to create a VUI on the Web? § Three (main) steps, typically: o Speech Recognition o Text manipulation (e.g., Natural Language Processing) o Speech Synthesis § We are going to start from a simple application to reach a quite complex scenario o by using HTML5, JS, and PHP § Reminder: we are interested in creating an interactive prototype, at the end 2 Human Computer Interaction Weather Web App A VUI for "chatting" about the weather Base implementation at https://github.com/polito-hci-2019/vui-example 3 Human Computer Interaction Speech Recognition and Synthesis § Web Speech API o currently a draft, experimental, unofficial HTML5 API (!) o https://wicg.github.io/speech-api/ § Covers both speech recognition and synthesis o different degrees of support by browsers 4 Human Computer Interaction Web Speech API: Speech Recognition § Accessed via the SpeechRecognition interface o provides the ability to recogniZe voice from an audio input o normally via the device's default speech recognition service § Generally, the interface's constructor is used to create a new SpeechRecognition object § The SpeechGrammar interface can be used to represent a particular set of grammar that your app should recogniZe o Grammar is defined using JSpeech Grammar Format (JSGF) 5 Human Computer Interaction Speech Recognition: A Minimal Example const recognition = new window.SpeechRecognition(); recognition.onresult = (event) => { const speechToText = event.results[0][0].transcript; -
A Multimodal User Interface for an Assistive Robotic Shopping Cart
electronics Article A Multimodal User Interface for an Assistive Robotic Shopping Cart Dmitry Ryumin 1 , Ildar Kagirov 1,* , Alexandr Axyonov 1, Nikita Pavlyuk 1, Anton Saveliev 1, Irina Kipyatkova 1, Milos Zelezny 2, Iosif Mporas 3 and Alexey Karpov 1,* 1 St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia; [email protected] (D.R.); [email protected] (A.A.); [email protected] (N.P.); [email protected] (A.S.); [email protected] (I.K.) 2 Department of Cybernetics, Faculty of Applied Sciences, University of West Bohemia, 301 00 Pilsen, Czech Republic; [email protected] 3 School of Engineering and Computer Science, University of Hertfordshire, Hatfield, Herts AL10 9AB, UK; [email protected] * Correspondence: [email protected] (I.K.); [email protected] (A.K.) Received: 2 November 2020; Accepted: 5 December 2020; Published: 8 December 2020 Abstract: This paper presents the research and development of the prototype of the assistive mobile information robot (AMIR). The main features of the presented prototype are voice and gesture-based interfaces with Russian speech and sign language recognition and synthesis techniques and a high degree of robot autonomy. AMIR prototype’s aim is to be used as a robotic cart for shopping in grocery stores and/or supermarkets. Among the main topics covered in this paper are the presentation of the interface (three modalities), the single-handed gesture recognition system (based on a collected database of Russian sign language elements), as well as the technical description of the robotic platform (architecture, navigation algorithm). -
Eindversie-Paper-Rianne-Nieland-2057069
Talking to Linked Data: Comparing voice interfaces for generalpurpose data Master thesis of Information Sciences Rianne Nieland Vrije Universiteit Amsterdam [email protected] ABSTRACT ternet access (Miniwatts Marketing Group, 2012) and People in developing countries cannot access informa- 31.1% is literate (UNESCO, 2010). tion on the Web, because they have no Internet access and are often low literate. A solution could be to pro- A solution to the literacy and Internet access problem vide voice-based access to data on the Web by using the is to provide voice-based access to the Internet by us- GSM network. Related work states that Linked Data ing the GSM network (De Boer et al., 2013). 2G mobile could be a useful input source for such voice interfaces. phones are digital mobile phones that use the GSM net- work (Fendelman, 2014). In Africa the primary mode The aim of this paper is to find an efficient way to make of telecommunication is mobile telephony (UNCTAD, general-purpose data, like Wikipedia information, avail- 2007). able using voice interfaces for GSM. To achieve this, we developed two voice interfaces, one for Wikipedia This paper is about how information on the Web ef- and one for DBpedia, by doing requirements elicitation ficiently can be made available using voice interfaces from literature and developing a voice user interface and for GSM. We developed two voice interfaces, one us- conversion algorithms for Wikipedia and DBpedia con- ing Wikipedia and the other using DBpedia. In this cepts. With user tests the users evaluated the two voice paper we see Wikipedia and DBpedia as two different interfaces, to be able to compare them. -
Voice Assistants and Smart Speakers in Everyday Life and in Education
Informatics in Education, 2020, Vol. 19, No. 3, 473–490 473 © 2020 Vilnius University, ETH Zürich DOI: 10.15388/infedu.2020.21 Voice Assistants and Smart Speakers in Everyday Life and in Education George TERZOPOULOS, Maya SATRATZEMI Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece Email: [email protected], [email protected] Received: November 2019 Abstract. In recent years, Artificial Intelligence (AI) has shown significant progress and its -po tential is growing. An application area of AI is Natural Language Processing (NLP). Voice as- sistants incorporate AI by using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporates them in households nowadays. Most common devices with voice assistants are smart speakers and they have just started to be used in schools and universities. The purpose of this paper is to study how voice assistants and smart speakers are used in everyday life and whether there is potential in order for them to be used for educational purposes. Keywords: artificial intelligence, smart speakers, voice assistants, education. 1. Introduction Emerging technologies like virtual reality, augmented reality and voice interaction are reshaping the way people engage with the world and transforming digital experiences. Voice control is the next evolution of human-machine interaction, thanks to advances in cloud computing, Artificial Intelligence (AI) and the Internet of Things (IoT). In the last years, the heavy use of smartphones led to the appearance of voice assistants such as Apple’s Siri, Google’s Assistant, Microsoft’s Cortana and Amazon’s Alexa. -
Models of Speech Synthesis ROLF CARLSON Department of Speech Communication and Music Acoustics, Royal Institute of Technology, S-100 44 Stockholm, Sweden
Proc. Natl. Acad. Sci. USA Vol. 92, pp. 9932-9937, October 1995 Colloquium Paper This paper was presented at a colloquium entitled "Human-Machine Communication by Voice," organized by Lawrence R. Rabiner, held by the National Academy of Sciences at The Arnold and Mabel Beckman Center in Irvine, CA, February 8-9,1993. Models of speech synthesis ROLF CARLSON Department of Speech Communication and Music Acoustics, Royal Institute of Technology, S-100 44 Stockholm, Sweden ABSTRACT The term "speech synthesis" has been used need large amounts of speech data. Models working close to for diverse technical approaches. In this paper, some of the the waveform are now typically making use of increased unit approaches used to generate synthetic speech in a text-to- sizes while still modeling prosody by rule. In the middle of the speech system are reviewed, and some of the basic motivations scale, "formant synthesis" is moving toward the articulatory for choosing one method over another are discussed. It is models by looking for "higher-level parameters" or to larger important to keep in mind, however, that speech synthesis prestored units. Articulatory synthesis, hampered by lack of models are needed not just for speech generation but to help data, still has some way to go but is yielding improved quality, us understand how speech is created, or even how articulation due mostly to advanced analysis-synthesis techniques. can explain language structure. General issues such as the synthesis of different voices, accents, and multiple languages Flexibility and Technical Dimensions are discussed as special challenges facing the speech synthesis community.