{"id":24243,"date":"2023-03-03T23:25:52","date_gmt":"2023-03-03T22:25:52","guid":{"rendered":"https:\/\/blog.mi.hdm-stuttgart.de\/?p=24243"},"modified":"2023-06-26T23:22:39","modified_gmt":"2023-06-26T21:22:39","slug":"modern-application-of-voice-ai-technology","status":"publish","type":"post","link":"https:\/\/blog.mi.hdm-stuttgart.de\/index.php\/2023\/03\/03\/modern-application-of-voice-ai-technology\/","title":{"rendered":"Modern application of Voice AI technology"},"content":{"rendered":"\n
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With the advancement of technology and the gradually increasing use of artificial intelligence, new markets are developed. One of such is the market of Voice AI which became a commercial success with voice bots such as Alexa or Siri. They were mainly used as digital assistants who could answer questions, set reminders and they could generally tap into various databases to provide the aid and service that they were ask to provide. Although the popular use case seems to be primarily domestic, we have long since experienced other applications for Voice AI in areas such as UI speech control, voice recognition and replication and in use within entertainment media.<\/p>\n\n\n\n

Faced with the ever-present but ever-growing interest in artificial intelligence which continue to further their influence on society, industry and the commercial landscape, my post will strive to demonstrate and inspect the technologies surrounding the application of Voice AI but also the concurrent obstacles it needs to overcome. However, before we can dive deeper into the topic, it is important to introduce the concept of Voice AI and its general technological workings.<\/p>\n\n\n\n\n\n\n\n

About Voice AI<\/h3>\n\n\n\n

The definition of Voice AI is not yet set in stone and it can vary based on the scope of application. What we can most likely agree on is that Voice AI is a component of conversational AI which the users can interact with. With a combination of machine learning and natural language processing, Voice AI can analyze language and speech in order to produce sound.[1]<\/sup> It can provide the user with a way to interact with the system in an auditory way as the system tries to interpret the human speech via voice recognition. In other cases the Voice AI technology can be used to generate human-like speech which is either pre-written by the user as a text-to-speech approach or as a result of trained AI that systematically responds with generated answers.[2]<\/sup> In either case, the Voice AI technology is bound to be a service that the users can make use of. This is especially true if we take into account the aspects of embedded system and enabling a hands-free user experience. For the purpose of keeping this blog post concise and on point, this post will demonstrate and inspect one application of Voice AI that is publicly available. <\/p>\n\n\n\n

Voice AI Synthesis via VALL-E<\/h2>\n\n\n\n

AI TTS, also known as neural text-to-speech, makes use of neural networks and machine learning technologies. In a nutshell, the speech engine analyzes the audio input of a human voice via automatic speech recognition (ASR), and then try to understand the meaning of the words it has collected via natural-language generation (NLG). With the inclusion of neural networks, the artificial intelligence is able to learn the style of how people communicate with one another including response patterns and conversational flow. After the generation of the text that is intended to be converted into speech, a speech synthesis model is used to articulate and read the text.[3]<\/sup><\/p>\n\n\n\n

Right this moment the world is confronted with countless AI-generated voice-overs of popular figures which are flooding the internet. The infusion of AI and deep learning has revolutionized the TTS (text-to-speech) synthesis procedure to produce lifelike speech variations and severely decrease the “robotic”-like inflexion and pronunciation.[4]<\/sup> In a demonstration shown below, you will find a user-created conversation between two Voice AI models that were taught with voice samples of real media figures in order to perfectly mimic voice, inflexion and tone that is fitting for conversation:<\/p>\n\n\n\n

\"Overview<\/a>
Figure 1<\/strong>: VALL-E’s pipeline consists of phoneme conversion and sampling in the voice input data, converting it into discrete code which is then decoded into a waveform.<\/figcaption><\/figure>\n\n\n\n

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A github page<\/a> was published to show the features of VALL-E’s advanced speech synthesis and speech generation methods for research demonstration purposes. These include:<\/p>\n\n\n\n