Abstract: This disclosure describes a system that analyzes a corpus of text (e.g., a financial article, an audio book, etc.) so that the context surrounding the text is fully understood. For instance, the context may be an environment described by the text, or an environment in which the text occurs. Based on the analysis, the system can determine sentiment, part of speech, entities, and/or human characters at the token level of the text, and automatically generate Speech Synthesis Markup Language (SSML) tags based on this information. The SSML tags can be used by applications, services, and/or features that implement text-to-speech (TTS) conversion to improve the audio experience for end-users. Consequently, via the techniques described herein, more realistic and human-like speech synthesis can be efficiently implemented at larger scale (e.g., for audio books, for all the articles published to a news site, etc.).