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Commerical TTS - Google, Amazon, Apple and Microsoft (2010s)
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== Key Innovations == === Microsoft === The first application of speech synthesis by Microsoft was in Windows 2000, with the release of Microsoft Narrator<ref name=":0" />. Narrator was subsequently improved to sound more natural, such as through formant analysis using [[Hidden Markov Models in Speech Synthesis|Hidden Markov Models]]<ref name=":1" /> and other [[Advancements in Neural Network-Based TTS (2000s)|advancements in neural network-based TTS]]. Microsoft also showed one of the first bilingual speech synthesis systems, called Mulan<ref>Chu, M., Peng, H., Zhao, Y., Niu, Z., & Chang, E. (2003, April). Microsoft Mulan-a bilingual TTS system. In ''2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03).'' (Vol. 1, pp. I-I). IEEE. </ref>. Mulan is a model that can switch between Mandarin and English within a sentence, maintaining the intonation and voice quality. This was a major development for text-to-speech, especially as the combination of tonal and stress languages normally proves to be difficult. The application of this technology was according to the authors mostly to allow for more natural sounding switching between English terms in Chinese text. In light of the [[Introduction of Voice Assistants|introduction of voice assistants]], Microsoft released [https://www.microsoft.com/en-us/cortana Cortana] in April of 2014. Cortana was a personal digital assistant using speech recognition for user input and speech synthesis for system output, and was based on the Microsoft Speech API. However, by the end of 2023, Microsoft phased out Cortana out of most of its services in favour of Bing Chat AI and Windows Copilot<ref>https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa</ref>. === Google === At the advent of WaveNet in 2016, Google updated all their existing applications to better fit with the new TTS model that had hit the markets. Google Translate transitioned to a neural machine translation premise, which updated its capabilities in translating for grammar and larger sentences, while also allowing for Google Translate to better pronounce the translated phrases to the recipient.<ref>This is how Google Translate actually works. (2021, March 24). The Independent. https://www.independent.co.uk/tech/how-does-google-translate-work-b1821775.html</ref> This continued with the creation of Google Assistant: a smoother Personal Assistant that provided faster information and a much more pleasant, engaging experience than its competitors in Alexa and Siri.<ref>Lynley, M. (2016, May 18). Google unveils Google Assistant, a virtual assistant that’s a big upgrade to Google Now. TechCrunch. https://techcrunch.com/2016/05/18/google-unveils-google-assistant-a-big-upgrade-to-google-now/</ref> The release of Google Assistant, done in the same year that WaveNet was revealed, also prompted the reveal of Google Home, an ambient voice-recognition interface for Google Assistant based at an individual home, which became a direct competitor to Amazon's Alexa. Finally, in 2017, Google released Google Cloud Speech API, which allowed developers to transmute text into human-like speech in more than 180 voices across more than 30 variant languages, and has gone through multiple revisions since then, adding more support and languages to the API for use.<ref>Barri, E., Gkamas, A., Michos, E., Bouras, C., Koulouri, C., & Salgado, S. A. K. (2020). Text to Speech through Bluetooth for People with Special Needs Navigation.</ref> === Apple === Apple begun its venture with text-to-speech (TTS) technology in the 1980s with the introduction of the MacInTalk text-to-speech system on the Macintosh in 1984. This early effort paved the way for the integration of TTS technology in Apple's ecosystem, making digital text audibly accessible to users. Over the decades, Apple continued to refine and expand its TTS capabilities within its PlainTalk package, showcasing a commitment to making its platforms more inclusive and user-friendly. Notable advancements include the introduction of high-quality voices and multilingual support, which significantly improved the user experience. With the surge of mobile technology, Apple leveraged its TTS technology to further enhance accessibility on its iOS devices. Siri, introduced in 2011, embodied a major leap in Apple's TTS and speech recognition capabilities, allowing users to interact with their devices using natural language<ref>Capes, T., Coles, P., Conkie, A., Golipour, L., Hadjitarkhani, A., Hu, Q., ... & Zhang, H. (2017, August). Siri on-device deep learning-guided unit selection text-to-speech system. In ''Interspeech'' (pp. 4011-4015).</ref>. The integration of TTS technology in various Apple applications and features, such as VoiceOver, Speak Selection, and Speak Screen, has demonstrated Apple's ongoing efforts to support individuals with visual impairments and learning disabilities. Through consistent innovation, Apple has played a pivotal role in advancing TTS technology and promoting its application in enhancing accessibility and user interaction across its product lineup. === Amazon === The Kindle 2 was just the tip of the iceberg. While it allowed text content to be read aloud, the voice was mechanical, lacking human-like intonations. Amazon Polly was the next significant milestone. Launched in 2016 as part of AI functionalities of Amazon Web Services (AWS)<ref>https://techcrunch.com/2016/11/30/amazon-launches-amazon-ai-to-bring-its-machine-learning-smarts-to-developers/</ref>. Polly leveraged deep learning to produce lifelike speech. It supported multiple languages and offered a plethora of voices. Beyond simple reading, Polly could whisper<ref>https://aws.amazon.com/blogs/aws/new-amazon-polly-speech-marks/</ref>, vary speech rates, and even emulate different styles<ref>https://aws.amazon.com/blogs/aws/amazon-polly-introduces-neural-text-to-speech-and-newscaster-style/</ref>. Another major stride was with Alexa, Amazon's virtual assistant, introduced in 2014<ref>Luger, E., & Sellen, A. (2016, May). " Like Having a Really Bad PA" The Gulf between User Expectation and Experience of Conversational Agents. In Proceedings of the 2016 CHI conference on human factors in computing systems (pp. 5286-5297).</ref>. Alexa's voice interactions were driven by sophisticated TTS engines. The innovation didn’t stop with English. Alexa was soon multilingual, speaking in German, Japanese, and other languages<ref>https://techcrunch.com/2019/10/11/alexa-now-speaks-spanish-including-in-multi-lingual-mode/</ref>. Voice User Interfaces (VUIs) were another addition, wherein the device could recognize individual users and customize responses<ref>Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018, April). Voice interfaces in everyday life. In proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-12).</ref>. This personalization made interactions more user-centric and increased user engagement.
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