From Markov chains to music diffusion: a speech reading list

What it does This repository is a curated markdown bibliography of academic papers spanning automatic speech recognition, speech synthesis, speaker verification, voice conversion, and generative audio. It collects PDF links for landmark works from 1982 through 2023, organizing them under topic headings like ASR, TTS, and text-to-audio generation. Think of it as a single bookmark file for the entire speech-AI literature stack.

The interesting bit The list quietly documents the field’s full paradigm shift: it starts with 1980s hidden Markov model tutorials and ends with 2023 diffusion models like AudioLDM and MusicLM. That breadth makes it useful as a historical map, not just a reading list.

Key highlights

• Coverage spans nine sub-fields, including singing voice synthesis, confidence estimation, and music modeling.
• Includes foundational papers (Rabiner’s 1989 HMM tutorial, Graves’s 2006 CTC paper) alongside recent diffusion and attention work.
• Direct PDF links are provided for most entries, usually via arXiv, IEEE, or Semantic Scholar.
• Organized chronologically within each section, so you can watch the field evolve from classical signal processing to end-to-end neural networks.

Caveats

• Entries are bare titles and links: there are no summaries, annotations, or difficulty ratings to guide newcomers.
• The list is extremely long; the README truncates in the source, suggesting hundreds of entries and a lot of scrolling.
• It is a reading list, not a framework or dataset—there is no code to run.

Verdict Researchers and graduate students drowning in speech-literature FOMO should bookmark this. If you are looking for executable models or annotated course notes, look elsewhere.