ai speech generator

ai speech generator

The Power of AI Speech Generation

1. Introduction to AI Speech Generation

Traditionally, speech has been a challenge for computers to understand and generate. Natural language processing (NLP) has made great strides in recent years, allowing for effective and reliable trips to be arranged and web search queries to be understood. Where these inputs provide structured data into a computer, the data for understanding and generating human speech is unstructured. Understanding spoken language with its variations in sound is difficult for software to interpret. When it comes to generation, a computer needs to capture the complexity of human speech in order to produce something that isn’t flat and robotic sounding. The difference between speech recognition, analyzing spoken language and transcribing it into text, and speech generation, synthesizing spoken language from written text, is the difference between a software understanding and a software production.

AI-generated speech is a machine production of human speech. At the moment, they are implemented in virtual assistants, such as Alexa, Google Home, and Siri. Many companies are starting to use AI-generated speech for customer service call centers. Unlike pre-recorded prompts, AI speech can provide natural and more complex interactions. Examples include asking for a specific type of music on a radio, requesting an on-demand service’s help-line, interacting with a robotic customer service representative, and more. To a less successful extent, AI-generated speech has even been implemented in language learning computer applications, for the purpose of teaching a foreign language to an English speaker.

2. Benefits of AI Speech Generation

Developments in AI language interpretation have provided the ability to translate between languages, as well as the ability to interpret human language to a computer language and vice versa. This has many potential benefits for human communication. Simulation of natural language interpretation can enable a computer to act as an interpreter between two people, transforming one person’s speech in one language to the second person, and translating each response back to the original speaker. This also has implications for the design of future AI systems that assist humans. These systems can use language interpretation routines to understand the intentions of a user more effectively. The ability to interpret human language can also open the door to more sophisticated communication between man and machine. For example, a machine could reason about a user’s input in a natural language form, by interpreting the input into a logical form, and then transforming that logical form into different conclusions in natural language before presenting the conclusions back to the user.

B. Language interpretation

AI technology has led to tremendous improvements in creating and developing languages. With AI, it is possible to create computer languages and pseudo languages to aid the development of a human language. Using an AI technique called genetic programming, software can be made to evolve a language to solve a specific task. When the language is no longer effective in solving the problem, the AI can evolve the language to be more effective. AI has also been used to develop and expand existing languages, with the most widely known example being the development and expansion of Esperanto.

A. Creation and development of languages

3. Applications of AI Speech Generation

The MARY (Modular Architecture for Research on speech synthesis) is an effort to unite various research groups who have been working in the area of text-to-speech synthesis using differing methods and languages, to compare techniques and share resources. The end result will be a system capable of multilingual speech synthesis in multiple voices with adjustable personality. This is hoped to be a valuable resource to developers of new applications using speech synthesis, and thus lead to a wider variety of speech-enabled services. This is just one of the examples of where AI speech synthesis can be applied, and technology such as MARY will likely act as a catalyst to a new era of more widespread, intelligent and natural sounding synthesized speech.

As of 2020, the text-to-speech and speech synthesis industry still remains one of the most feature-poor areas of application of AI technology. Despite this, a number of companies have been offering cutting-edge products with proprietary technology. One of the most common applications of speech synthesis has been in the use of screen readers for the visually impaired, though today’s text-to-speech systems have often been criticized for a robotic sound or lack of intelligibility. However, significant progress has been made in this field with the latest generation of synthesized voice, exemplified by the MARY system.

4. Challenges and Limitations of AI Speech Generation

New research is focusing on using the articulatory or acoustic model to directly predict sounds, which would avoid the decoding stage and vastly simplify the process. AI research on the articulatory model aims to recreate the human vocal tract to generate sound, which is a physically and linguistically complex task. While the acoustic model is less complex and has shown good results, because it uses no linguistic knowledge, it is essentially a regression of the given data, so it will not cope well with new words or accents.

The most promising modern technique for speech generation is parametric synthesis. This method uses HMMs and linguistic knowledge to synthesize speech from scratch. This allows for much flexibility and can potentially cover all sentences with a single system. However, it is an extremely complex and difficult task to get right, and current parametric synthesis often sounds robotic and mispronounces words. HMMs are trained from a database of speech data aligned with the correct text. Using this method, the HMMs will produce a continuous vector sequence which, when decoded, will generate speech. This process can produce a voice that sounds natural, but due to its complexity, it is impossible to correct mistakes in the produced vector.

Current research on AI speech generation faces many hurdles before it can achieve human parity. The most widely used method for speech generation is concatenative synthesis, which strings together recorded speech sounds to make sentences. Concatenative synthesis can give very natural results; however, it is inflexible and stilted because it is difficult to create the many different recordings necessary to cover all possible sentences. Currently, it is only practical to use concatenative synthesis for short sentences. For a system to cover all possible sentences with recorded speech, it would need a huge database of speech sounds with many different speakers. Also, the creation of new sentences not already recorded can cause unnatural sound as it will use sounds from other sentences, and time stretching or pitch scaling will degrade quality.

5. The Future of AI Speech Generation

This is by no means the end of AI speech generation, and if anything, we are just barely scratching the surface of how useful it can be and the impact it will have on people’s lives. In fact, for this reason, the progress achieved so far is likely still far behind its time, since it is still relatively difficult to obtain and a luxury for most people who are not disabled. With the increasing complexity of technology and the aim to make human lives more convenient by automating simple yet tedious tasks, I would argue that creating AI voices that inherently sound engaging and lifelike will soon become a feature that’s essential in things like digital personal assistants or audiobooks. Even something like having a GPS with a human-like AI voice has the potential to be very useful. TTS has long been used on GPS, but the current voices come nowhere close to the standard that will likely be achieved in the future. From there, in the bigger picture for entertainment or convenience tools, it’s not hard to imagine AI voices being integrated not only into various software and games but also into things like online content for guided lessons or stories. For this idea, a very real example of what I am talking about is the recent development of VOICEROID and its different versions developed by AH Software. These are all applications where the user inputs some form of text and the AI reads it out. While still far from perfect in its current state, these could be seen as a prototype of a much more advanced form of AI speech generation to come. Another thing to consider is the potential globalization of internet technology on the issue of language barriers. With the progress of AI speech generation, we could have tools for real-time translation of audio at an unprecedented quality. Logically, this would mean that people who speak vastly different languages could still interact more fluidly by means such as online gaming, which is to date still largely limited to those who speak the same language due to the poor quality of existing translation tools.