Commerical TTS - Google, Amazon, Apple and Microsoft (2010s): Difference between revisions

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== Historical Context ==
== Historical Context ==
=== Microsoft ===
=== Microsoft ===
Microsoft seriously entered the field of text-to-speech with the recruitment of the speech scientist Dr. Xuedong Huang, one of developers of the SPHINX-II system, a successor of [[Carnegie Mellon's Harpy System]]<ref>Huang, X., Alleva, F., Hon, H. W., Hwang, M. Y., Lee, K. F., & Rosenfeld, R. (1993). The SPHINX-II speech recognition system: an overview. ''Computer Speech & Language'', ''7''(2), 137-148.</ref>. With Huang at the wheel, Microsoft developed their Speech Application Programming Interface (API), for speech recognition and speech synthesis applications inside the Windows 2000 and Windows XP operating systems<ref>https://en.wikipedia.org/wiki/Microsoft_Speech_API</ref><ref>Këpuska, V., & Bohouta, G. (2017). Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx). ''Int. J. Eng. Res. Appl'', ''7''(03), 20-24.</ref>. An early application from this line of development is Microsoft Sam, a male text-to-speech voice that was used by the Microsoft Narrator, a screen reading application that is meant as an accessibility feature for the visually impaired<ref name=":0">https://support.microsoft.com/en-us/windows/complete-guide-to-narrator-e4397a0d-ef4f-b386-d8ae-c172f109bdb1</ref>. The development of more natural sounding text-to-speech systems then moved to Whistler, a trainable system that aimed to move away from concatenative and formant synthesis methods by generalizing from data instead <ref>Huang, X., Acero, A., Adcock, J., Hon, H. W., Goldsmith, J., Liu, J., & Plumpe, M. (1996, October). Whistler: A trainable text-to-speech system. In ''Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96'' (Vol. 4, pp. 2387-2390). IEEE.</ref>. Other advancements included [[Hidden Markov Models in Speech Synthesis|Hidden Markov Models]] and later Microsoft moved to [[Deep Learning Revolution|deep neural networks]] like others<ref name=":1">Acero, A. (1999). Formant analysis and synthesis using hidden Markov models. In ''Sixth European Conference on Speech Communication and Technology''.</ref><ref>Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. ''IEEE Transactions on audio, speech, and language processing'', ''20''(1), 30-42.</ref><ref>https://azure.microsoft.com/en-us/blog/microsoft-s-new-neural-text-to-speech-service-helps-machines-speak-like-people/</ref>. Later versions of Windows added more natural sounding voices with different accents to Narrator and their other services, but the Microsoft Speech API remains a key part in their development.  
Microsoft seriously entered the field of text-to-speech with the recruitment of the speech scientist Dr. Xuedong Huang, one of developers of the SPHINX-II system, a successor of [[Carnegie Mellon's Harpy System]]<ref>Huang, X., Alleva, F., Hon, H. W., Hwang, M. Y., Lee, K. F., & Rosenfeld, R. (1993). The SPHINX-II speech recognition system: an overview. ''Computer Speech & Language'', ''7''(2), 137-148.</ref>. With Huang at the wheel, Microsoft developed their Speech Application Programming Interface (API), for speech recognition and speech synthesis applications inside the Windows 2000 and Windows XP operating systems<ref>https://en.wikipedia.org/wiki/Microsoft_Speech_API</ref><ref>Këpuska, V., & Bohouta, G. (2017). Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx). ''Int. J. Eng. Res. Appl'', ''7''(03), 20-24.</ref>. An early application from this line of development is Microsoft Sam, a male text-to-speech voice that was used by the Microsoft Narrator, a screen reading application that is meant as an accessibility feature for the visually impaired<ref name=":0">https://support.microsoft.com/en-us/windows/complete-guide-to-narrator-e4397a0d-ef4f-b386-d8ae-c172f109bdb1</ref>. The development of more natural sounding text-to-speech systems then moved to Whistler, a trainable system that aimed to move away from concatenative and formant synthesis methods by generalizing from data instead <ref>Huang, X., Acero, A., Adcock, J., Hon, H. W., Goldsmith, J., Liu, J., & Plumpe, M. (1996, October). Whistler: A trainable text-to-speech system. In ''Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96'' (Vol. 4, pp. 2387-2390). IEEE.</ref>. Other advancements included [[Hidden Markov Models in Speech Synthesis|Hidden Markov Models]] and later Microsoft moved to [[Deep Learning Revolution|deep neural networks]] like others<ref name=":1">Acero, A. (1999). Formant analysis and synthesis using hidden Markov models. In ''Sixth European Conference on Speech Communication and Technology''.</ref><ref>Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. ''IEEE Transactions on audio, speech, and language processing'', ''20''(1), 30-42.</ref><ref>https://azure.microsoft.com/en-us/blog/microsoft-s-new-neural-text-to-speech-service-helps-machines-speak-like-people/</ref>. Later versions of Windows added more natural sounding voices with different accents to Narrator and their other services, but the Microsoft Speech API remains a key part in their development.
 
=== Google ===
Google began exploring text-to-speech(TTS) in earnest when it acquired the British-based artificial company, [[DeepMind]], in January 2014.<ref>Hodson, H. (n.d.). DeepMind and Google: The battle to control artificial intelligence.</ref> Though DeepMind maintains a portion of control over its intellectual property, exclusive of Google, the foundations derived from the subsidiary have helped create some of the widely recognized devices that utilize TTS today. In  2016, DeepMind revealed to the world WaveNet, a deep neural network for generating raw audio waveforms.<ref>Oord, A. van den, Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio (arXiv:1609.03499). arXiv. https://doi.org/10.48550/arXiv.1609.03499</ref> Following the release of WaveNet, Google began to create and refine multiple products, utilizing WaveNet’s model, including Google Assistant, released in the same year, and eventually Google Cloud Speech API, released in 2017. The power that WaveNet had over other TTS models was clear, as Google Assistant began to take the stage as the forefront of Intelligent Personal Assistants, overtaking both Siri and Cortant, and only being matched by Alexa in some regards.<ref>Berdasco, A., López, G., Diaz, I., Quesada, L., & Guerrero, L. A. (2019). User Experience Comparison of Intelligent Personal Assistants: Alexa, Google Assistant, Siri and Cortana. Proceedings, 31(1), Article 1. https://doi.org/10.3390/proceedings2019031051</ref>


== Key Innovations ==
== Key Innovations ==

Revision as of 18:05, 17 October 2023

Introduction

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Historical Context

Microsoft

Microsoft seriously entered the field of text-to-speech with the recruitment of the speech scientist Dr. Xuedong Huang, one of developers of the SPHINX-II system, a successor of Carnegie Mellon's Harpy System[1]. With Huang at the wheel, Microsoft developed their Speech Application Programming Interface (API), for speech recognition and speech synthesis applications inside the Windows 2000 and Windows XP operating systems[2][3]. An early application from this line of development is Microsoft Sam, a male text-to-speech voice that was used by the Microsoft Narrator, a screen reading application that is meant as an accessibility feature for the visually impaired[4]. The development of more natural sounding text-to-speech systems then moved to Whistler, a trainable system that aimed to move away from concatenative and formant synthesis methods by generalizing from data instead [5]. Other advancements included Hidden Markov Models and later Microsoft moved to deep neural networks like others[6][7][8]. Later versions of Windows added more natural sounding voices with different accents to Narrator and their other services, but the Microsoft Speech API remains a key part in their development.

Google

Google began exploring text-to-speech(TTS) in earnest when it acquired the British-based artificial company, DeepMind, in January 2014.[9] Though DeepMind maintains a portion of control over its intellectual property, exclusive of Google, the foundations derived from the subsidiary have helped create some of the widely recognized devices that utilize TTS today. In 2016, DeepMind revealed to the world WaveNet, a deep neural network for generating raw audio waveforms.[10] Following the release of WaveNet, Google began to create and refine multiple products, utilizing WaveNet’s model, including Google Assistant, released in the same year, and eventually Google Cloud Speech API, released in 2017. The power that WaveNet had over other TTS models was clear, as Google Assistant began to take the stage as the forefront of Intelligent Personal Assistants, overtaking both Siri and Cortant, and only being matched by Alexa in some regards.[11]

Key Innovations

Microsoft

The first application of speech synthesis by Microsoft was in Windows 2000, with the release of Microsoft Narrator[4]. Narrator was subsequently improved to sound more natural, such as through formant analysis using Hidden Markov Models[6]. Microsoft also showed one of the first bilingual speech synthesis systems, called Mulan[12]. 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, Microsoft released 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[13].

Impact

Microsoft

Although Narrator itself did not become very popular as an accessibility feature, the dominance of Windows in the operating systems market and the dire need for good screen readers manifested a breeding ground for the development of screen readers, in particular the popular Job Access With Speech (JAWS) and NonVisual Desktop Access (NVDA)[14]. This has had an enormous impact on the speech synthesis field for accessibility applications and the computer usage of visually impaired people.

Future research

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LLM Review

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Contributors

Brandi Hongell, Ömer Tarik Özyilmaz, Jocomin Galarneau, Xiaoling (River) Lin, Yuxing (Patrick) Ouyang

References

  1. Huang, X., Alleva, F., Hon, H. W., Hwang, M. Y., Lee, K. F., & Rosenfeld, R. (1993). The SPHINX-II speech recognition system: an overview. Computer Speech & Language, 7(2), 137-148.
  2. https://en.wikipedia.org/wiki/Microsoft_Speech_API
  3. Këpuska, V., & Bohouta, G. (2017). Comparing speech recognition systems (Microsoft API, Google API and CMU Sphinx). Int. J. Eng. Res. Appl, 7(03), 20-24.
  4. 4.0 4.1 https://support.microsoft.com/en-us/windows/complete-guide-to-narrator-e4397a0d-ef4f-b386-d8ae-c172f109bdb1
  5. Huang, X., Acero, A., Adcock, J., Hon, H. W., Goldsmith, J., Liu, J., & Plumpe, M. (1996, October). Whistler: A trainable text-to-speech system. In Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP'96 (Vol. 4, pp. 2387-2390). IEEE.
  6. 6.0 6.1 Acero, A. (1999). Formant analysis and synthesis using hidden Markov models. In Sixth European Conference on Speech Communication and Technology.
  7. Dahl, G. E., Yu, D., Deng, L., & Acero, A. (2011). Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Transactions on audio, speech, and language processing, 20(1), 30-42.
  8. https://azure.microsoft.com/en-us/blog/microsoft-s-new-neural-text-to-speech-service-helps-machines-speak-like-people/
  9. Hodson, H. (n.d.). DeepMind and Google: The battle to control artificial intelligence.
  10. Oord, A. van den, Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: A Generative Model for Raw Audio (arXiv:1609.03499). arXiv. https://doi.org/10.48550/arXiv.1609.03499
  11. Berdasco, A., López, G., Diaz, I., Quesada, L., & Guerrero, L. A. (2019). User Experience Comparison of Intelligent Personal Assistants: Alexa, Google Assistant, Siri and Cortana. Proceedings, 31(1), Article 1. https://doi.org/10.3390/proceedings2019031051
  12. 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.
  13. https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa
  14. https://www.theverge.com/23203911/screen-readers-history-blind-henter-curran-teh-nvda