Commerical TTS - Google, Amazon, Apple and Microsoft (2010s)

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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.

In the 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[9].

Key Innovations

Microsoft

Impact

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Future research

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LLM Review

References

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Contributors

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

  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. 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. 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. https://support.microsoft.com/en-us/topic/end-of-support-for-cortana-d025b39f-ee5b-4836-a954-0ab646ee1efa